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Real

real

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 3 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References 3. SwitchLight We introduce SwitchLight, a state-of-the-art framework for human portrait relighting, with its architectural overview presented in Fig. 2. We first provide foundational concepts in Sec. 3.1, and define the problem in Sec. 3.2. This is followed by detailing the architecture in Sec. 3.3, and lastly, we describe the loss functions used in Sec. 3.4. 3.1. Preliminaries In this section, vectors n, v, l, and h are denoted as unit vectors. Specifically, n represents the surface normal, v is the view direction, l is the incident light direction, and h is the half-vector computed from l and v. The dot product is clamped between [0..1], indicated by ⟨·⟩. Image Rendering. The primary goal of image rendering is to create a visual representation that accurately simulates the interactions between light and surfaces. These complex interactions are encapsulated by the rendering equation: A surface intrinsically exhibits both diffuse and specular reflections. The diffuse component uniformly scatters light, ensuring consistent illumination regardless of the viewing angle. In contrast, the specular component is viewing angle-dependent, producing shiny highlights that are crucial for achieving a photorealistic effect. Lambertian Diffuse Reflectance. Lambertian reflectance, a standard model for diffuse reflection, describes a uniform light scatter irrespective of the viewing angle. This ensures a consistent appearance in brightness: Here, σ is the albedo, indicating the intrinsic color and brightness of the surface. Cook-Torrance Specular Reflectance. The Cook-Torrance model, based on microfacet theory, represents surfaces as a myriad of tiny, mirror-like facets. It incorporates a roughness parameter α, which allows precise rendering of surface specular reflectance: In this model, D is the microfacet distribution function, describing the orientation of the microfacets relative to the half-vector h, G is the geometric attenuation factor, accounting for the shadowing and masking of microfacets, and F is the Fresnel term, calculating the reflectance variation depending on the viewing angle, where f0 is the surface Fresnel reflectivity at normal incidence. A lower α value implies a smoother surface with sharper specular highlights, whereas a higher α value indicates a rougher surface, resulting in more diffused reflections. By adjusting α, the Cook-Torrance model can depict a range of specular reflections. Image Formation. Upon the base rendering equation, we include the diffuse and specular components of the BRDF and derive a unified formula: where E(l) denotes the incident environmental lighting. This formula represents the core principle that an image is a product of interplay between the BRDF and lighting. To further clarify this concept, we introduce a rendering function R, succinctly modeling the process of image formation: It is important to note that since the BRDF is a function of surface properties, as detailed in Eqn. 3 and 4, we can now clearly understand that image formation is essentially governed by the interaction of surface attributes and lighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 3 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 3 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 3 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 3 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References 3. SwitchLight We introduce SwitchLight, a state-of-the-art framework for human portrait relighting, with its architectural overview presented in Fig. 2. We first provide foundational concepts in Sec. 3.1, and define the problem in Sec. 3.2. This is followed by detailing the architecture in Sec. 3.3, and lastly, we describe the loss functions used in Sec. 3.4. 3.1. Preliminaries In this section, vectors n, v, l, and h are denoted as unit vectors. Specifically, n represents the surface normal, v is the view direction, l is the incident light direction, and h is the half-vector computed from l and v. The dot product is clamped between [0..1], indicated by ⟨·⟩. Image Rendering. The primary goal of image rendering is to create a visual representation that accurately simulates the interactions between light and surfaces. These complex interactions are encapsulated by the rendering equation: Image Rendering. A surface intrinsically exhibits both diffuse and specular reflections. The diffuse component uniformly scatters light, ensuring consistent illumination regardless of the viewing angle. In contrast, the specular component is viewing angle-dependent, producing shiny highlights that are crucial for achieving a photorealistic effect. Lambertian Diffuse Reflectance. Lambertian reflectance, a standard model for diffuse reflection, describes a uniform light scatter irrespective of the viewing angle. This ensures a consistent appearance in brightness: Lambertian Diffuse Reflectance. Here, σ is the albedo , indicating the intrinsic color and brightness of the surface. albedo Cook-Torrance Specular Reflectance. The Cook-Torrance model, based on microfacet theory, represents surfaces as a myriad of tiny, mirror-like facets. It incorporates a roughness parameter α, which allows precise rendering of surface specular reflectance: Cook-Torrance Specular Reflectance. In this model, D is the microfacet distribution function, describing the orientation of the microfacets relative to the half-vector h, G is the geometric attenuation factor, accounting for the shadowing and masking of microfacets, and F is the Fresnel term, calculating the reflectance variation depending on the viewing angle, where f0 is the surface Fresnel reflectivity at normal incidence. A lower α value implies a smoother surface with sharper specular highlights, whereas a higher α value indicates a rougher surface, resulting in more diffused reflections. By adjusting α, the Cook-Torrance model can depict a range of specular reflections. Image Formation. Upon the base rendering equation, we include the diffuse and specular components of the BRDF and derive a unified formula: Image Formation. where E(l) denotes the incident environmental lighting. This formula represents the core principle that an image is a product of interplay between the BRDF and lighting. To further clarify this concept, we introduce a rendering function R, succinctly modeling the process of image formation: It is important to note that since the BRDF is a function of surface properties, as detailed in Eqn. 3 and 4, we can now clearly understand that image formation is essentially governed by the interaction of surface attributes and lighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

This New Tech Makes Faces in Photos Look Incredibly Realistic

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 1 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References Abstract We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model, we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore, to overcome the limitation of scarce high-quality lightstage data, we have developed a selfsupervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism. 1. Introduction Relighting is more than an aesthetic tool; it unlocks infinite narrative possibilities and enables seamless integration of subjects into diverse environments (see Fig. 1). This advancement resonates with our innate desire to transcend the physical constraints of space and time, while also providing tangible solutions to practical challenges in digital content creation. It is particularly transformative in virtual (VR) and augmented reality (AR) applications, where relighting facilitates the real-time adaptation of lighting, ensuring that users and digital elements coexist naturally within any environment, offering a next level of telepresence. In this work, we focus on human portrait relighting. While the relighting task fundamentally demands an indepth understanding of geometry, material properties, and illumination, the challenge is more compounded when addressing human subjects, due to the unique characteristics of skin surfaces as well as the diverse textures and reflectance properties of a wide array of clothing, hairstyles, and accessories. These elements interact in complex ways, necessitating advanced algorithms capable of simulating the subtle interplay of light with these varied surfaces. Currently, the most promising approach involves the use of deep neural networks trained on pairs of high-quality relit portrait images and their corresponding intrinsic attributes, which are sourced from a light stage setup [10]. Initial efforts approached the relighting process as a ‘black box’ [45, 48], without delving into the underlying mechanisms. Later advancements adopted a physics-guided model design, incorporating the explicit modeling of image intrinsics and image formation physics [32]. Pandey et al. [34] proposed the Total Relight (TR) architecture, also physics-guided, which decomposes an input image into surface normals and albedo maps, and performs relighting based on the Phong specular reflectance model. The TR architecture has become foundational model for image relighting, with most recent and advanced architectures building upon its principle [23, 31, 52]. Following the physics-guided approach, our contribution lies in a co-design of architecture with a self-supervised pre-training framework. First, our architecture evolves towards a more accurate physical model by integrating the Cook-Torrance specular reflectance model [8], representing a notable advancement from the empirical Phong specular model [37] employed in the Total Relight architecture. The Cook-Torrance model adeptly simulates light interactions with surface microfacets, accounting for spatially varying roughness and reflectivity. Secondly, our pretraining framework scales the learning process beyond the typically hard-to-obtain lightstage data. By revisiting the masked autoencoder (MAE) framework [19], we adept it for the task of relighting. These modifications are crafted to address the unique challenges posed by this task, enabling our model to learn from unlabelled data and refine its ability to produce realistic relit portraits during fine-tuning. To the best of our knowledge, this is the first time applying self-supervised pre-training specifically to the relighting task. To summarize, our contribution is twofold. Firstly, by enhancing the physical reflectance model, we have introduced a new level of realism in the output. Secondly, by adopting self-supervised learning, we have expanded the scale of the training data and enhanced the expression of lighting in diverse real-world scenarios. Collectively, these advancements have led SwitchLight framework to achieve a new state-of-the-art in human portrait relighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 1 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 1 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 1 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 1 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References Abstract We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model, we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore, to overcome the limitation of scarce high-quality lightstage data, we have developed a selfsupervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism. We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model, we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore, to overcome the limitation of scarce high-quality lightstage data, we have developed a selfsupervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism. 1. Introduction Relighting is more than an aesthetic tool; it unlocks infinite narrative possibilities and enables seamless integration of subjects into diverse environments (see Fig. 1). This advancement resonates with our innate desire to transcend the physical constraints of space and time, while also providing tangible solutions to practical challenges in digital content creation. It is particularly transformative in virtual (VR) and augmented reality (AR) applications, where relighting facilitates the real-time adaptation of lighting, ensuring that users and digital elements coexist naturally within any environment, offering a next level of telepresence. In this work, we focus on human portrait relighting. While the relighting task fundamentally demands an indepth understanding of geometry, material properties, and illumination, the challenge is more compounded when addressing human subjects, due to the unique characteristics of skin surfaces as well as the diverse textures and reflectance properties of a wide array of clothing, hairstyles, and accessories. These elements interact in complex ways, necessitating advanced algorithms capable of simulating the subtle interplay of light with these varied surfaces. Currently, the most promising approach involves the use of deep neural networks trained on pairs of high-quality relit portrait images and their corresponding intrinsic attributes, which are sourced from a light stage setup [10]. Initial efforts approached the relighting process as a ‘black box’ [45, 48], without delving into the underlying mechanisms. Later advancements adopted a physics-guided model design, incorporating the explicit modeling of image intrinsics and image formation physics [32]. Pandey et al. [34] proposed the Total Relight (TR) architecture, also physics-guided, which decomposes an input image into surface normals and albedo maps, and performs relighting based on the Phong specular reflectance model. The TR architecture has become foundational model for image relighting, with most recent and advanced architectures building upon its principle [23, 31, 52]. Following the physics-guided approach, our contribution lies in a co-design of architecture with a self-supervised pre-training framework. First, our architecture evolves towards a more accurate physical model by integrating the Cook-Torrance specular reflectance model [8], representing a notable advancement from the empirical Phong specular model [37] employed in the Total Relight architecture. The Cook-Torrance model adeptly simulates light interactions with surface microfacets, accounting for spatially varying roughness and reflectivity. Secondly, our pretraining framework scales the learning process beyond the typically hard-to-obtain lightstage data. By revisiting the masked autoencoder (MAE) framework [19], we adept it for the task of relighting. These modifications are crafted to address the unique challenges posed by this task, enabling our model to learn from unlabelled data and refine its ability to produce realistic relit portraits during fine-tuning. To the best of our knowledge, this is the first time applying self-supervised pre-training specifically to the relighting task. To summarize, our contribution is twofold. Firstly, by enhancing the physical reflectance model, we have introduced a new level of realism in the output. Secondly, by adopting self-supervised learning, we have expanded the scale of the training data and enhanced the expression of lighting in diverse real-world scenarios. Collectively, these advancements have led SwitchLight framework to achieve a new state-of-the-art in human portrait relighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

New Framework by Beeble Researchers Promises to Bring Realistic Glow to Digital Portraits Using AI

Nuwe raamwerk deur Beeble Researchers belowe om realistiese gloed na digitale portrette te bring met behulp van AI

Machaq Marco Beeble Yatxatirinakax AI apnaqasa Retratos Digitales ukar chiqpach qhant’ayañ arsuwayi

Un nuevo marco de trabajo de los investigadores de Beeble promete aportar un brillo realista a los retratos digitales mediante inteligencia artificial

מסגרת חדשה מאת Beeble Researchers מבטיחה להביא זוהר ריאליסטי לפורטרטים דיגיטליים באמצעות AI

Beeble-ի հետազոտողների նոր շրջանակը խոստանում է իրատեսական փայլ հաղորդել թվային դիմանկարներին՝ օգտագործելով AI

Beeble 研究者による新しいフレームワークは、AI を使用してデジタル ポートレートにリアルな輝きをもたらすことを約束します

Cadre ya sika ya ba chercheurs ya Beeble elaki ko mema éclat réaliste na ba portraits numériques en utilisant AI

Ny rafitra vaovao nataon'ny Beeble Researchers dia mampanantena fa hitondra hazavana tena misy amin'ny sary nomerika amin'ny fampiasana AI

Новая платформа от исследователей Beeble обещает придать реалистичное свечение цифровым портретам с помощью ИИ

Ibikorwa bishya byakozwe nabashakashatsi ba Beeble basezeranya kuzana umucyo nyawo ku mashusho ya Digital ukoresheje AI

Chimiro Chitsva neBeeble Vanotsvaga Vanovimbisa Kuunza Kupenya Kwechokwadi kune Digital Portraits Uchishandisa AI.

Rimba Lerintshwa hi Valavisisi va Beeble Ri Tshembisa ku Tisa Ku Vonakala ka Xiviri eka Swifaniso swa Dijitali hi ku Tirhisa AI

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References 2. Related Work Human Portrait Relighting is an ill-posed problem due to its under-constrained nature. To tackle this, earlier methods incorporated 3D facial priors [44], exploited image intrinsics [3, 40], or framed the task as a style transfer [43]. Light stage techniques [49] offer a more powerful solution by recording subject’s reflectance fields under varying lighting conditions [10, 14], though they are labor-intensive and require specialized equipment. A promising alternative has emerged with deep learning, utilizing neural networks trained on light stage data. Sun et al. [45] pioneered this approach, but their method had limitations in representing non-Lambertian effects. This was improved upon by Nestmeyer et al. [32], who integrated rendering physics into network design, albeit limited to directional light. Building upon this, Pandey et al. [34] incorporated the Phong reflection model and a high dynamic range (HDR) lighting map [9] into their network, enabling a more accurate representation of global illumination. Simultaneously, efforts have been made to explore portrait relighting without light stage data [20, 21, 42, 47, 55]. Moreover, introduction of NeRF [7] and diffusion-based [38] models has opened new avenues in the field. However, networks trained with lightstage data maintain superior accuracy and realism, thanks to physics-based composited relight image training pairs and precise ground truth image intrinsics [56]. Our work furthers this domain by integrating the Cook-Torrance model into our network design, shifting from the empirical Phong model to a more physics-based approach, thereby enhancing the realism and detail in relit images. Self-supervised Pre-training has become a standard training scheme in the development of large language models like BERT [11] and GPT [39], and is increasingly influential in vision models, aiming to replicate the ‘BERT moment’. This approach typically involves pre-training on extensive unlabeled data, followed by fine-tuning on specific tasks. While early efforts in vision models focused on simple pretext tasks [13, 17, 33, 36, 53], the field has evolved through stages like contrastive learning [5, 18] and masked image modeling [2, 19, 50]. However, the primary focus has remained on visual recognition, with less attention to other domains. Exceptions include low-level image processing tasks [4, 6, 27, 30] using the vision transformer [15]. Our research takes a different route, focusing on human portrait relighting—a complex challenge of manipulating illumination in the image. This direction is crucial because acquiring accurate ground truth data, especially from light stage, is both expensive and difficult. We modify the MAE framework [19], previously successful in robust image representation learning and developing locality biases [35], to suit the unique requirements of effective relighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References 2. Related Work Human Portrait Relighting is an ill-posed problem due to its under-constrained nature. To tackle this, earlier methods incorporated 3D facial priors [44], exploited image intrinsics [3, 40], or framed the task as a style transfer [43]. Light stage techniques [49] offer a more powerful solution by recording subject’s reflectance fields under varying lighting conditions [10, 14], though they are labor-intensive and require specialized equipment. A promising alternative has emerged with deep learning, utilizing neural networks trained on light stage data. Sun et al. [45] pioneered this approach, but their method had limitations in representing non-Lambertian effects. This was improved upon by Nestmeyer et al. [32], who integrated rendering physics into network design, albeit limited to directional light. Building upon this, Pandey et al. [34] incorporated the Phong reflection model and a high dynamic range (HDR) lighting map [9] into their network, enabling a more accurate representation of global illumination. Simultaneously, efforts have been made to explore portrait relighting without light stage data [20, 21, 42, 47, 55]. Moreover, introduction of NeRF [7] and diffusion-based [38] models has opened new avenues in the field. However, networks trained with lightstage data maintain superior accuracy and realism, thanks to physics-based composited relight image training pairs and precise ground truth image intrinsics [56]. Human Portrait Relighting Our work furthers this domain by integrating the Cook-Torrance model into our network design, shifting from the empirical Phong model to a more physics-based approach, thereby enhancing the realism and detail in relit images. Self-supervised Pre-training has become a standard training scheme in the development of large language models like BERT [11] and GPT [39], and is increasingly influential in vision models, aiming to replicate the ‘BERT moment’. This approach typically involves pre-training on extensive unlabeled data, followed by fine-tuning on specific tasks. While early efforts in vision models focused on simple pretext tasks [13, 17, 33, 36, 53], the field has evolved through stages like contrastive learning [5, 18] and masked image modeling [2, 19, 50]. However, the primary focus has remained on visual recognition, with less attention to other domains. Exceptions include low-level image processing tasks [4, 6, 27, 30] using the vision transformer [15]. Self-supervised Pre-training Our research takes a different route, focusing on human portrait relighting—a complex challenge of manipulating illumination in the image. This direction is crucial because acquiring accurate ground truth data, especially from light stage, is both expensive and difficult. We modify the MAE framework [19], previously successful in robust image representation learning and developing locality biases [35], to suit the unique requirements of effective relighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Beeble Researchers Use Physics-Based Models to Achieve Realistic Lighting Effects in Images

Weather Image Recognition with AI: Dataset Labeling, Model Training, and Real-world Usecases

Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References 2 Related Work Early text-to-image generative models, such as DALL-E (Ramesh et al., 2021) and CogView (Ding et al., 2021), or later Parti (Yu et al., 2022) employed autoregressive approaches but often suffered from significant content-level artifacts. This led to the development of a new breed of models that utilized the diffusion process to enhance image quality. Diffusion-based models, such as DALLE 2 (Ramesh et al., 2022), Imagen (Saharia et al., 2022b), and Stable Diffusion[6], have since become cornerstones in this domain. These models are typically divided into pixel-level (Ramesh et al., 2022; Saharia et al., 2022b) and latent-level (Rombach et al., 2022) approaches. This surge of interest has led to the design of innovative approaches and architectures, paving the way for numerous applications based on opensource generative models, such as DreamBooth (Ruiz et al., 2023) and DreamPose (Karras et al., 2023). These applications exploit image generation techniques to offer remarkable features, further fueling the popularity and the rapid development of diffusion-based image generation approaches. This enabled a wide array of applications like 3D object synthesis (Poole et al., 2023; Tang et al., 2023; Lin et al., 2022; Chen et al., 2023), video generation (Ho et al., 2022b; Luo et al., 2023; Ho et al., 2022a; Singer et al., 2023; Blattmann et al., 2023; Esser et al., 2023), controllable image editing (Hertz et al., 2023; Parmar et al., 2023; Liew et al., 2022; Mou et al., 2023; Lu et al., 2023), and more which are now at the forefront of this domain. Diffusion models achieve state-of-the-art results in image generation task both unconditional (Ho et al., 2020; Nichol and Dhariwal, 2021) and conditional (Peebles and Xie, 2022). They beat GANs (Goodfellow et al., 2014) by generating images with better scores of fidelity and diversity without adversarial training (Dhariwal and Nichol, 2021). Diffusion models also show the best performance in various image processing tasks like inpainting, outpainting, and super-resolution (Batzolis et al., 2021; Saharia et al., 2022a). Text-to-image diffusion models have become a popular research direction due to the high performance of diffusion models and the ability to simply integrate text conditions with the classifierfree guidance algorithm (Ho and Salimans, 2022). Early models like GLIDE (Nichol et al., 2022), Imagen (Saharia et al., 2022b), DALL-E 2 (Ramesh et al., 2022) and eDiff-I (Balaji et al., 2022) generate low-resolution image in pixel space and then upsample it with another super-resolution diffusion models. They are also using different text encoders, large language model T5 (Raffel et al., 2020) in Imagen, CLIP (Radford et al., 2021) in GLIDE and DALL-E 2. This paper is available on arxiv under CC BY 4.0 DEED license. [6] https://github.com/CompVis/stable-diffusion Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 2 Related Work Early text-to-image generative models, such as DALL-E (Ramesh et al., 2021) and CogView (Ding et al., 2021), or later Parti (Yu et al., 2022) employed autoregressive approaches but often suffered from significant content-level artifacts. This led to the development of a new breed of models that utilized the diffusion process to enhance image quality. Diffusion-based models, such as DALLE 2 (Ramesh et al., 2022), Imagen (Saharia et al., 2022b), and Stable Diffusion[6], have since become cornerstones in this domain. These models are typically divided into pixel-level (Ramesh et al., 2022; Saharia et al., 2022b) and latent-level (Rombach et al., 2022) approaches. This surge of interest has led to the design of innovative approaches and architectures, paving the way for numerous applications based on opensource generative models, such as DreamBooth (Ruiz et al., 2023) and DreamPose (Karras et al., 2023). These applications exploit image generation techniques to offer remarkable features, further fueling the popularity and the rapid development of diffusion-based image generation approaches. This enabled a wide array of applications like 3D object synthesis (Poole et al., 2023; Tang et al., 2023; Lin et al., 2022; Chen et al., 2023), video generation (Ho et al., 2022b; Luo et al., 2023; Ho et al., 2022a; Singer et al., 2023; Blattmann et al., 2023; Esser et al., 2023), controllable image editing (Hertz et al., 2023; Parmar et al., 2023; Liew et al., 2022; Mou et al., 2023; Lu et al., 2023), and more which are now at the forefront of this domain. Diffusion models achieve state-of-the-art results in image generation task both unconditional (Ho et al., 2020; Nichol and Dhariwal, 2021) and conditional (Peebles and Xie, 2022). They beat GANs (Goodfellow et al., 2014) by generating images with better scores of fidelity and diversity without adversarial training (Dhariwal and Nichol, 2021). Diffusion models also show the best performance in various image processing tasks like inpainting, outpainting, and super-resolution (Batzolis et al., 2021; Saharia et al., 2022a). Text-to-image diffusion models have become a popular research direction due to the high performance of diffusion models and the ability to simply integrate text conditions with the classifierfree guidance algorithm (Ho and Salimans, 2022). Early models like GLIDE (Nichol et al., 2022), Imagen (Saharia et al., 2022b), DALL-E 2 (Ramesh et al., 2022) and eDiff-I (Balaji et al., 2022) generate low-resolution image in pixel space and then upsample it with another super-resolution diffusion models. They are also using different text encoders, large language model T5 (Raffel et al., 2020) in Imagen, CLIP (Radford et al., 2021) in GLIDE and DALL-E 2. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [6] https://github.com/CompVis/stable-diffusion

Russian Scientists Identify Why Older Text-to-Image Models Couldn’t Create Realistic Images

Ella Isn’t Real, But She Feels Real: The Empathic AI Leasing Agent in Our Looming Bot First Economy

ኤላ እውነት አይደለችም፣ ነገር ግን እውነት ይሰማታል፡ የEmpathic AI የሊዝ ወኪል በእኛ ሎሚንግ ቦት የመጀመሪያ ኢኮኖሚ

Эла не сапраўдная, але адчувае сябе сапраўднай: эмпатычны агент па арэндзе штучнага інтэлекту ў нашай будучай эканоміцы Bot First

Ella no es real, pero parece real: la agente de arrendamiento con inteligencia artificial empática en nuestra inminente economía basada en bots

Ella Tidak Nyata, Namun Ia Terasa Nyata: Agen Penyewaan AI yang Empatik di Era Ekonomi Bot First yang Akan Datang

エラは実在しないが、実在するように感じさせる: 迫りくるボット優先経済における共感力のある AI リース エージェント

Элла шынайы емес, бірақ ол өзін шынайы сезінеді: біздің жақындап келе жатқан бірінші экономикамыздағы эмпатикалық AI лизинг агенті

Ella Tidak Nyata, Tetapi Dia Terasa Nyata: Ejen Pemajakan AI Empatik dalam Ekonomi Pertama Bot Kami yang Menanti

एला वास्तविक होइन, तर उनी वास्तविक महसुस गर्छिन्: हाम्रो लुमिङ बोट फर्स्ट इकोनोमीमा एम्प्याथिक एआई लीजिङ एजेन्ट

Ella Isn’t Real, But She Feels Real: Moemedi wa Khiro ya AI yo a nago le Kwelobohloko ka go Ekonomi ya Rena ya Pele ya Bot yeo e Looming

ایلا ریښتیا نه ده ، مګر هغه ریښتیني احساس کوي: زموږ په لومینګ بوټ لومړی اقتصاد کې د ایمپاټیک AI اجارې اجنټ

Ella Ntabwo arukuri, ariko yumva arukuri: Umukozi wa Empathic AI ukodesha muri Bot yacu Yegereje Ubukungu Bwa mbere

Ella hakyky däl, ýöne özüni hakyky duýýar: Geljekdäki ilkinji ykdysadyýetimizde Empathic AI lizing agenti

Authors:
(1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution;
(2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.6 Additional Model Details 4 Empirical Evaluation and 4.1 Synthetic Tasks 4.2 Language Modeling 4.3 DNA Modeling 4.4 Audio Modeling and Generation 4.5 Speed and Memory Benchmarks 4.6 Model Ablations 5 Discussion 6 Conclusion and References A Discussion: Selection Mechanism B Related Work C Mechanics of Selective SSMs D Hardware-aware Algorithm For Selective SSMs E Experimental Details and Additional Results 3.6 Additional Model Details Real vs. Complex. Most prior SSMs use complex numbers in their state ℎ, which is necessary for strong performance on many tasks (Gu, Goel, and Ré 2022). However, it has been empirically observed that completely real-valued SSMs seem to work fine, and possibly even better, in some settings (Ma et al. 2023). We use real values as the default, which work well for all but one of our tasks; we hypothesize that the complex-real tradeoff is related to the continuous-discrete spectrum in data modalities, where complex numbers are helpful for continuous modalities (e.g. audio, video) but not discrete (e.g. text, DNA). Initialization. Most prior SSMs also suggest special initializations, particularly in the complex-valued case, which can help in several settings such as low-data regimes. Our default initialization for the complex case is S4D-Lin and for the real case is S4D-Real (Gu, Gupta, et al. 2022), which is based on the HIPPO theory (Gu, Dao, et al. 2020). These define the n-th element of A as −1∕2 + ni and −(n + 1) respectively. However, we expect many initializations to work fine, particularly in the large-data and real-valued SSM regimes; some ablations are considered in Section 4.6. Remark 3.1. For brevity in our experimental results, we sometimes abbreviate selective SSMs as S6 models, because they are S4 models with a selection mechanism and computed with a scan. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Authors: Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 State Space Models 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.5 Properties of Selection Mechanisms 3.6 Additional Model Details 3.6 Additional Model Details 4 Empirical Evaluation and 4.1 Synthetic Tasks 4 Empirical Evaluation and 4.1 Synthetic Tasks 4.2 Language Modeling 4.2 Language Modeling 4.3 DNA Modeling 4.3 DNA Modeling 4.4 Audio Modeling and Generation 4.4 Audio Modeling and Generation 4.5 Speed and Memory Benchmarks 4.5 Speed and Memory Benchmarks 4.6 Model Ablations 4.6 Model Ablations 5 Discussion 5 Discussion 6 Conclusion and References 6 Conclusion and References A Discussion: Selection Mechanism A Discussion: Selection Mechanism B Related Work B Related Work C Mechanics of Selective SSMs C Mechanics of Selective SSMs D Hardware-aware Algorithm For Selective SSMs D Hardware-aware Algorithm For Selective SSMs E Experimental Details and Additional Results E Experimental Details and Additional Results 3.6 Additional Model Details Real vs. Complex. Most prior SSMs use complex numbers in their state ℎ, which is necessary for strong performance on many tasks (Gu, Goel, and Ré 2022). However, it has been empirically observed that completely real-valued SSMs seem to work fine, and possibly even better, in some settings (Ma et al. 2023). We use real values as the default, which work well for all but one of our tasks; we hypothesize that the complex-real tradeoff is related to the continuous-discrete spectrum in data modalities, where complex numbers are helpful for continuous modalities (e.g. audio, video) but not discrete (e.g. text, DNA). Real vs. Complex. Initialization . Most prior SSMs also suggest special initializations, particularly in the complex-valued case, which can help in several settings such as low-data regimes. Our default initialization for the complex case is S4D-Lin and for the real case is S4D-Real (Gu, Gupta, et al. 2022), which is based on the HIPPO theory (Gu, Dao, et al. 2020). These define the n-th element of A as −1∕2 + ni and −(n + 1) respectively. However, we expect many initializations to work fine, particularly in the large-data and real-valued SSM regimes; some ablations are considered in Section 4.6. Initialization Remark 3.1. For brevity in our experimental results, we sometimes abbreviate selective SSMs as S6 models, because they are S4 models with a selection mechanism and computed with a scan. Remark 3.1. For brevity in our experimental results, we sometimes abbreviate selective SSMs as S6 models, because they are S4 models with a selection mechanism and computed with a scan. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

The Key Differences Between Real and Complex-Valued State Space Models

Oliver Rust, a veteran of data analytics with experience at Nielsen and the Engine Group, discusses how Truflation is transforming inflation measurement through advanced data collection and blockchain technology. In this interview, he shares insights on their innovative approach to tracking economic indicators and their recent expansion into the Indian market. Ishan Pandey: Hello Oliver, it's a pleasure to welcome you to our 'Behind the Startup' series. Please tell us about yourself and your journey with Truflation. Oliver Rust: I am a data guy who previously worked at Taylor Nelson Sofres, Nielsen and the Engine group and joined Truflation back in 2021. I was and still am excited by what Truflation can bring to the market: a more dynamic, transparent and faster delivery of financial and economic data sets designed for the decentralized marketplace. The applications of this data set are endless and provide many opportunities ahead. You have to be excited about that 🙂 Ishan Pandey: How do you see your approach as differing from traditional inflation-tracking methods like those used by government agencies? Oliver Rust: Truflation is so far ahead in measuring changes to inflation compared to government agencies, given that: Truflation makes the indexes available 30 times faster than current measurement tools and we report the indexes daily.


Truflation leverages over 80 data providers and aggregates worldwide, resulting in multiple data sources with different methodologies covering each category, creating a more representative and balanced measurement tool.


Truflation leverages census-level data, enabling us to access more than 40 million prices of goods and services compared to traditional indexes, which track roughly 100,000 items (varies by market)


Truflation uses open-source algorithms to make the data more transparent and deliver the unfiltered truth via our Truf Network, a decentralized platform, and


We measure the price changes from a consumer cost of living perspective. Ishan Pandey: Truflation recently launched a Hedge Index tracking Real World Assets (RWAs) to protect investments from inflation. Could you explain how it works and what its benefits are for investors? Oliver Rust: The Truflation Hedge Index is an insurance policy offsetting any losses from other investments and inflation. This index tracks the price movements of the five core asset classes: Gold, Silver, WTI Crude Oil, Bitcoin and S&P 500. The index fluctuates based on the underlying constituents' movements, calculated using the price movements based on the weighting contribution of each component. We brought this index live for the crypto community to be able to trade against and offset inflation and diversify individual portfolios. Ishan Pandey: Truflation is known for using real-time data to track inflation. How do you ensure the accuracy and relevance of this data in an ever-changing economic landscape? What tools or technologies do you rely on to support this? Oliver Rust: At Truflation we are used to handling census-level data and we have a 7 points quality control process that we have automated and deployed on the truf network that allows us to provide accurate data reflecting the price changes consumers are experiencing from a cost of living perspective. On top of this, leveraging a minimum of 3 data sources per index allows us to remove any dependency on one data set and provide more comprehensiveness to our indexes. Ishan Pandey: With your presence at India Blockchain Week (IBW), how does Truflation view the growing blockchain ecosystem in India? How do blockchain technology impact inflation tracking and financial services in this market? Oliver Rust: Being at India Blockchain week is buzzing here and exciting. There is so much energy and positivity to make change for the good that the community and their actions can not energize you. I am just happy that we are going to be part of it. Everybody needs accurate, reliable and transparent data, and we look forward to bringing this to the market in India. Ishan Pandey: Blockchain regulation in India is still evolving. How does Truflation navigate regulatory challenges in a market like India? Oliver Rust: Our focus is to provide information to the crypto community that they can leverage to create financial products from the economic and financial data that we provide through Truf Network. That is our focus at present. Ishan Pandey: At IBW, we see innovative blockchain projects' convergence. Are there any specific blockchain startups or institutions you are particularly excited to collaborate with in India? How can these partnerships enhance Truflation's product offerings? Oliver Rust: Having been at IBW it is exciting just to see the amount of innovative ideas and entrepreneurs that are launching so many new products in the market. This ranges from the gaming industry all the way through to data applications. This myriad of new solutions being brought to the marketplace is so exciting. We are lucky to partner with Levitate, which has backed so many start-ups that we are excited and eager to get going! Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Oliver Rust, a veteran of data analytics with experience at Nielsen and the Engine Group, discusses how Truflation is transforming inflation measurement through advanced data collection and blockchain technology. In this interview, he shares insights on their innovative approach to tracking economic indicators and their recent expansion into the Indian market. Truflation Ishan Pandey: Hello Oliver, it's a pleasure to welcome you to our 'Behind the Startup' series. Please tell us about yourself and your journey with Truflation. Ishan Pandey: Oliver Rust: I am a data guy who previously worked at Taylor Nelson Sofres, Nielsen and the Engine group and joined Truflation back in 2021. I was and still am excited by what Truflation can bring to the market: a more dynamic, transparent and faster delivery of financial and economic data sets designed for the decentralized marketplace. The applications of this data set are endless and provide many opportunities ahead. You have to be excited about that 🙂 Oliver Rust: Ishan Pandey: How do you see your approach as differing from traditional inflation-tracking methods like those used by government agencies? Ishan Pandey: Oliver Rust: Truflation is so far ahead in measuring changes to inflation compared to government agencies, given that: Oliver Rust: Truflation makes the indexes available 30 times faster than current measurement tools and we report the indexes daily. Truflation leverages over 80 data providers and aggregates worldwide, resulting in multiple data sources with different methodologies covering each category, creating a more representative and balanced measurement tool. Truflation leverages census-level data, enabling us to access more than 40 million prices of goods and services compared to traditional indexes, which track roughly 100,000 items (varies by market) Truflation uses open-source algorithms to make the data more transparent and deliver the unfiltered truth via our Truf Network, a decentralized platform, and We measure the price changes from a consumer cost of living perspective. Truflation makes the indexes available 30 times faster than current measurement tools and we report the indexes daily. Truflation makes the indexes available 30 times faster than current measurement tools and we report the indexes daily. Truflation leverages over 80 data providers and aggregates worldwide, resulting in multiple data sources with different methodologies covering each category, creating a more representative and balanced measurement tool. Truflation leverages over 80 data providers and aggregates worldwide, resulting in multiple data sources with different methodologies covering each category, creating a more representative and balanced measurement tool. Truflation leverages census-level data, enabling us to access more than 40 million prices of goods and services compared to traditional indexes, which track roughly 100,000 items (varies by market) Truflation leverages census-level data, enabling us to access more than 40 million prices of goods and services compared to traditional indexes, which track roughly 100,000 items (varies by market) Truflation uses open-source algorithms to make the data more transparent and deliver the unfiltered truth via our Truf Network, a decentralized platform, and Truflation uses open-source algorithms to make the data more transparent and deliver the unfiltered truth via our Truf Network, a decentralized platform, and We measure the price changes from a consumer cost of living perspective. We measure the price changes from a consumer cost of living perspective. Ishan Pandey: Truflation recently launched a Hedge Index tracking Real World Assets (RWAs) to protect investments from inflation. Could you explain how it works and what its benefits are for investors? Ishan Pandey: Oliver Rust: The Truflation Hedge Index is an insurance policy offsetting any losses from other investments and inflation. This index tracks the price movements of the five core asset classes: Gold, Silver, WTI Crude Oil, Bitcoin and S&P 500. The index fluctuates based on the underlying constituents' movements, calculated using the price movements based on the weighting contribution of each component. We brought this index live for the crypto community to be able to trade against and offset inflation and diversify individual portfolios. Oliver Rust: Ishan Pandey: Truflation is known for using real-time data to track inflation. How do you ensure the accuracy and relevance of this data in an ever-changing economic landscape? What tools or technologies do you rely on to support this? Ishan Pandey: Oliver Rust: At Truflation we are used to handling census-level data and we have a 7 points quality control process that we have automated and deployed on the truf network that allows us to provide accurate data reflecting the price changes consumers are experiencing from a cost of living perspective. On top of this, leveraging a minimum of 3 data sources per index allows us to remove any dependency on one data set and provide more comprehensiveness to our indexes. Oliver Rust: Ishan Pandey: With your presence at India Blockchain Week (IBW), how does Truflation view the growing blockchain ecosystem in India? How do blockchain technology impact inflation tracking and financial services in this market? Ishan Pandey: Oliver Rust: Being at India Blockchain week is buzzing here and exciting. There is so much energy and positivity to make change for the good that the community and their actions can not energize you. I am just happy that we are going to be part of it. Everybody needs accurate, reliable and transparent data, and we look forward to bringing this to the market in India. Oliver Rust: Ishan Pandey: Blockchain regulation in India is still evolving. How does Truflation navigate regulatory challenges in a market like India? Ishan Pandey: Oliver Rust: Our focus is to provide information to the crypto community that they can leverage to create financial products from the economic and financial data that we provide through Truf Network. That is our focus at present. Oliver Rust: Ishan Pandey: At IBW, we see innovative blockchain projects' convergence. Are there any specific blockchain startups or institutions you are particularly excited to collaborate with in India? How can these partnerships enhance Truflation's product offerings? Ishan Pandey: Oliver Rust: Having been at IBW it is exciting just to see the amount of innovative ideas and entrepreneurs that are launching so many new products in the market. This ranges from the gaming industry all the way through to data applications. This myriad of new solutions being brought to the marketplace is so exciting. We are lucky to partner with Levitate, which has backed so many start-ups that we are excited and eager to get going! Oliver Rust: Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

Innovation in Economic Data: Truflation's Oliver Rust on Real-Time Inflation Tracking

Інавацыі ў эканамічных дадзеных: Олівер Раст з Truflation аб адсочванні інфляцыі ў рэжыме рэальнага часу

Inovacija u ekonomskim podacima: Truflation Oliver Rust o praćenju inflacije u realnom vremenu

Innovación en datos económicos: Oliver Rust de Truflation habla sobre el seguimiento de la inflación en tiempo real

経済データのイノベーション: Truflation の Oliver Rust が語るリアルタイムのインフレ追跡

Иновации во економските податоци: Оливер Руст на Truflation за следење на инфлацијата во реално време

आर्थिक डेटामा नवीनता: वास्तविक-समय मुद्रास्फीति ट्र्याकिङमा ट्रुफ्लेसनको ओलिभर रस्ट

Qullqimanta Willakuykunapi musuqyachiy: Truflaciónpa Oliver Rust chiqap pacha Inflación qatipaypi

Инновации в экономических данных: Оливер Раст из Truflation об отслеживании инфляции в реальном времени

ආර්ථික දත්තවල නවෝත්පාදනය: තත්‍ය කාලීන උද්ධමනය ලුහුබැඳීම මත Truflation's Oliver Rust

Inovacioni në të dhënat ekonomike: Truflation's Oliver Rust në ndjekjen e inflacionit në kohë reale

Ubunifu katika Data ya Kiuchumi: Oliver Rust wa Truflation kwenye Ufuatiliaji wa Mfumuko wa Bei wa Wakati Halisi

Ukuqamba okusha kudatha yezomnotho: u-Oliver Rust we-Truflation ekulandeleleni ukwehla kwamandla emali ngesikhathi sangempela

Let's Build a Real-Time Collaborative Document Editor Using My Web App Framework of Choice

What Happens When Economic Models Fail to Predict Real Outcomes?

Was passiert, wenn es ökonomischen Modellen nicht gelingt, tatsächliche Ergebnisse vorherzusagen?

¿Qué sucede cuando los modelos económicos no logran predecir los resultados reales?

Mitä tapahtuu, kun talousmallit eivät pysty ennustamaan todellisia tuloksia?

Mi történik, ha a gazdasági modellek nem jósolják meg a valós eredményeket?

Apa yang Terjadi Ketika Model Ekonomi Gagal Memprediksi Hasil Nyata?

経済モデルが実際の結果を予測できない場合、何が起こるでしょうか?

Kas notiek, ja ekonomikas modeļi nespēj paredzēt reālos rezultātus?

Wat gebeurt er als economische modellen de werkelijke uitkomsten niet voorspellen?

O que acontece quando os modelos econômicos não conseguem prever resultados reais?

பொருளாதார மாதிரிகள் உண்மையான விளைவுகளைக் கணிக்கத் தவறினால் என்ன நடக்கும்?

จะเกิดอะไรขึ้นเมื่อแบบจำลองทางเศรษฐกิจไม่สามารถคาดการณ์ผลลัพธ์ที่แท้จริงได้?

当经济模型无法预测实际结果时会发生什么？

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