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The $30,000 Bequest, and Other Stories by Mark Twain, is part of the HackerNoon Books Series. You can jump to any chapter in this book . CHAPTER IX here CHAPTER IX At midnight they sat together, grieving, and the angel of the Lord appeared in the midst transfigured with a radiance not of earth; and speaking, said: “For liars a place is appointed. There they burn in the fires of hell from everlasting unto everlasting. Repent!” The bereaved fell upon their knees before him and clasped their hands and bowed their gray heads, adoring. But their tongues clove to the roof of their mouths, and they were dumb. “Speak! that I may bear the message to the chancery of heaven and bring again the decree from which there is no appeal.” Then they bowed their heads yet lower, and one said: “Our sin is great, and we suffer shame; but only perfect and final repentance can make us whole; and we are poor creatures who have learned our human weakness, and we know that if we were in those hard straits again our hearts would fail again, and we should sin as before. The strong could prevail, and so be saved, but we are lost.” They lifted their heads in supplication. The angel was gone. While they marveled and wept he came again; and bending low, he whispered the decree. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Mark Twain (2004). The $30,000 Bequest, and Other Stories. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/142/pg142-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

The angel of the Lord appeared in the midst transfigured with a radiance not of earth

The Beautiful and Damned: Book II, Chapter I - The Radiant Hour 

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The concept of trust has always been a somewhat fragile construction, akin to the finest silk cloth yet as strong as a knight's armor. It is that invisible glue that holds societies together, enabling human cooperation to scale from the family level right through to cities, nations, and global systems. But this eternal concept stands at a crossroads in the digital era. Its nature changing. With that come new complexities. Is trust the shining armor that fortifies the future or the scarlet stain marring our efforts to connect in a digitized world? The Old Guard: Relational Trust as Legacy For centuries, trust was relational; it was about building face-to-face. People shook hands with someone and looked someone in the eyes, and there was something physical about the trust given and received. This type of trust (let's call it "analog trust") represents a haptic experience. It requires personal investment, deep familiarity, and an echo of vulnerability. But even in its seeming purity, this trust was far from flawless. It was manipulable, as it was often a question of personal charisma or the level of hierarchy within the organizational structure that established someone's trustworthiness. The village elder, the priest, the banker—trust was usually intertwined with authority and sometimes to the harm of those least able to question it. This relational trust had, however, one indisputable strength: it could be resilient. It could be repaired, built anew, and enriched through shared experiences across time. The Dawn of Tech-Powered Trust: Armor With Cracks When digital trust as technology moved in, a model flipped on its head from the analog trust. In this case, trust wasn't culled through repeated, intimate human interactions but via algorithms, encryption, and smart contracts. Blockchain, DeFi, and decentralized technologies are promising a world of trust without intermediaries, one where code (not personal connective tissue) will ensure that everything remains fair. The shiny armor of digital trust is unbreakable transparency, immutability, and decentralization. This shining armor of promise also hides a great deal of flaws behind its glittering veneer. Tech-ensured trust is founded on a very flawed premise that systems cannot be corrupted and that technology is impartial. And as we have very well learned through faulty algorithms, hacking scandals, and the breakdown of decentralized governance, those cracks in the armor can be just as catastrophic as breaches in the analog world. The facelessness of the digital trust concept (where trustworthiness is only an equation) makes the failure feel more disillusioned when the system breaks. There is no one to blame; it is the very nature of trust in that context that becomes paradoxical because it can scale yet very well become increasingly brittle. Trust: Superpower or Weakness? One might say that trust, in its analog or digital forms, represents a weakness. It is the realization that we cannot control everything and, therefore, must rely on others or systems. In trusting, one opens oneself up to risk. It can be in the form of relational betrayal or technological breakdown within decentralized systems. Is trust, then, a vulnerability? Or is it, in fact, strength? Trust, in a decentralized world, is a superpower. Not because it makes us invincible but because it builds collective resilience. That's the beauty of this distributed system: it distributes the one thing needed - trust. No single entity can betray us because no single entity holds power. And it's this diffusion of trust that can make the collective stronger, the very act of trusting a form of empowerment. Equally, in relational trust, we often find strength, not in the moments when trust is upheld but in times when trust is broken and repaired. The real value of trust comes not from its perfection but from its ability to endure and adapt. Whether analog or digital, trust does not stand still. It is constantly in fluctuation, testing our judgment as to how far we want to trust the goodness of systems and people while simultaneously creating opportunities to rebuild an even stronger bond after such a moment of failure. Hybrid Trust: A Third Way? But maybe the most interesting evolution in trust is right at the crossroads of these two worlds: analog and digital. Hybrid trust, a blend of relational and tech-powered trust, is already a reality. Consider social media platforms where the trust is both relational (it's your network of friends and followers) and, at the same time, tech-driven, with algorithms curating what you see. Or consider decentralized finance, where trust in code and technology is complemented by trust in community governance and collaborative decision-making. This hybrid model might be a foreshadowing of what the future might hold in terms of moderating analogs open to manipulation and digitals to complete failure. Trust might be more like a multilayered construct, adaptive through a multiplicity of contexts fusing the personal with the technical. Conclusion: Armor Radiant, the Scarlet Stain Trusting, be it relational or powered with technology, is neither pure shining armor nor a scarlet stain. It is both, in a duality, that epitomizes the complication of human interconnectedness in the digital age. Though the methodology of building trust will change (evolving from handshake agreements to cryptographic signatures), the essence remains the same. I mean a delicate balance between risk and reward, vulnerability and strength. In this, I find the imperfections and possibilities of trust as a guiding force. One that even now shapes our actions in ways we're only beginning to understand. The concept of trust has always been a somewhat fragile construction, akin to the finest silk cloth yet as strong as a knight's armor. It is that invisible glue that holds societies together, enabling human cooperation to scale from the family level right through to cities, nations, and global systems. But this eternal concept stands at a crossroads in the digital era. Its nature changing. With that come new complexities. Is trust the shining armor that fortifies the future or the scarlet stain marring our efforts to connect in a digitized world? The Old Guard: Relational Trust as Legacy For centuries, trust was relational; it was about building face-to-face. People shook hands with someone and looked someone in the eyes, and there was something physical about the trust given and received. This type of trust (let's call it "analog trust") represents a haptic experience. It requires personal investment, deep familiarity, and an echo of vulnerability. But even in its seeming purity, this trust was far from flawless. It was manipulable, as it was often a question of personal charisma or the level of hierarchy within the organizational structure that established someone's trustworthiness. The village elder, the priest, the banker—trust was usually intertwined with authority and sometimes to the harm of those least able to question it. This relational trust had, however, one indisputable strength: it could be resilient. It could be repaired, built anew, and enriched through shared experiences across time. The Dawn of Tech-Powered Trust: Armor With Cracks When digital trust as technology moved in, a model flipped on its head from the analog trust. In this case, trust wasn't culled through repeated, intimate human interactions but via algorithms, encryption, and smart contracts. Blockchain, DeFi, and decentralized technologies are promising a world of trust without intermediaries, one where code (not personal connective tissue) will ensure that everything remains fair. The shiny armor of digital trust is unbreakable transparency, immutability, and decentralization. This shining armor of promise also hides a great deal of flaws behind its glittering veneer. Tech-ensured trust is founded on a very flawed premise that systems cannot be corrupted and that technology is impartial. And as we have very well learned through faulty algorithms, hacking scandals, and the breakdown of decentralized governance, those cracks in the armor can be just as catastrophic as breaches in the analog world. The facelessness of the digital trust concept (where trustworthiness is only an equation) makes the failure feel more disillusioned when the system breaks. There is no one to blame; it is the very nature of trust in that context that becomes paradoxical because it can scale yet very well become increasingly brittle. Trust: Superpower or Weakness? One might say that trust, in its analog or digital forms, represents a weakness. It is the realization that we cannot control everything and, therefore, must rely on others or systems. In trusting, one opens oneself up to risk. It can be in the form of relational betrayal or technological breakdown within decentralized systems. Is trust, then, a vulnerability? Or is it, in fact, strength? Trust, in a decentralized world, is a superpower. Not because it makes us invincible but because it builds collective resilience. That's the beauty of this distributed system: it distributes the one thing needed - trust. No single entity can betray us because no single entity holds power. And it's this diffusion of trust that can make the collective stronger, the very act of trusting a form of empowerment. Equally, in relational trust, we often find strength, not in the moments when trust is upheld but in times when trust is broken and repaired. The real value of trust comes not from its perfection but from its ability to endure and adapt. Whether analog or digital, trust does not stand still. It is constantly in fluctuation, testing our judgment as to how far we want to trust the goodness of systems and people while simultaneously creating opportunities to rebuild an even stronger bond after such a moment of failure. Hybrid Trust: A Third Way? But maybe the most interesting evolution in trust is right at the crossroads of these two worlds: analog and digital. Hybrid trust, a blend of relational and tech-powered trust, is already a reality. Consider social media platforms where the trust is both relational (it's your network of friends and followers) and, at the same time, tech-driven, with algorithms curating what you see. Or consider decentralized finance, where trust in code and technology is complemented by trust in community governance and collaborative decision-making. This hybrid model might be a foreshadowing of what the future might hold in terms of moderating analogs open to manipulation and digitals to complete failure. Trust might be more like a multilayered construct, adaptive through a multiplicity of contexts fusing the personal with the technical. Conclusion: Armor Radiant, the Scarlet Stain Trusting, be it relational or powered with technology, is neither pure shining armor nor a scarlet stain. It is both, in a duality, that epitomizes the complication of human interconnectedness in the digital age. Though the methodology of building trust will change (evolving from handshake agreements to cryptographic signatures), the essence remains the same. I mean a delicate balance between risk and reward, vulnerability and strength. In this, I find the imperfections and possibilities of trust as a guiding force. One that even now shapes our actions in ways we're only beginning to understand.

Trust in the Digital Age: The Shiny Armor and the Scarlet Stain

La confianza en la era digital: la armadura brillante y la mancha escarlata

Konfiantza Aro Digitalean: Armadura distiratsua eta Orban Escarlata

डिजिटल युग में भरोसा: चमकदार कवच और लाल रंग का दाग

Վստահություն թվային դարաշրջանում. փայլուն զրահը և կարմիր բիծը

デジタル時代の信頼：光り輝く鎧と緋色の染み

ជឿជាក់លើយុគសម័យឌីជីថល៖ គ្រឿងសឹកភ្លឺចាំង និងស្នាមប្រឡាក់ពណ៌ក្រហម

Дижитал эринд итгэх: Гялалзсан хуяг ба час улаан толбо

ඩිජිටල් යුගය කෙරෙහි විශ්වාසය: දිලිසෙන සන්නාහය සහ තද රතු පාට පැල්ලම

Sanly zamana ynam: Parlak ýaraglar we gyrmyzy tegmiller

Magtiwala sa Digital Age: The Shiny Armor and the Scarlet stain

Dijital Çağda Güven: Parlak Zırh ve Kızıl Leke

Niềm tin trong thời đại số: Bộ giáp sáng bóng và vết bẩn đỏ thẫm

A Nationwide Drive Time Matrix Between U.S. ZIP Code Areas: Methodology

Authors:
(1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk);
(2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk);
(3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk);
(4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk);
(5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Table of Links Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 3. Method 3.1. Training View Pre-processing 3.2. Progressive Training 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 5. Conclusion and 6. References ABSTRACT Current Neural Radiance Fields (NeRF) can generate photorealistic novel views. For editing 3D scenes represented by NeRF, with the advent of generative models, this paper proposes Inpaint4DNeRF to capitalize on state-of-the-art stable diffusion models (e.g., ControlNet [39]) for direct generation of the underlying completed background content, regardless of static or dynamic. The key advantages of this generative approach for NeRF inpainting are twofold. First, after rough mask propagation, to complete or fill in previously occluded content, we can individually generate a small subset of completed images with plausible content, called seed images, from which simple 3D geometry proxies can be derived. Second and the remaining problem is thus 3D multiview consistency among all completed images, now guided by the seed images and their 3D proxies. Without other bells and whistles, our generative Inpaint4DNeRF baseline framework is general which can be readily extended to 4D dynamic NeRFs, where temporal consistency can be naturally handled in a similar way as our multiview consistency 1. INTRODUCTION Recent development of Neural Radiance Fields (NeRF) [18] and its dynamic variants including [20, 25, 28] have shown their great potential in modeling scenes in 3D and 4D. This representation is suitable for scene editing from straightforward user inputs such as text prompts, eliminating the need for modeling and animating in detail. One important task in scene editing is generative inpainting, which refers to generating plausible content that is consistent with the background scene. Generative inpainting has a wide range of potential applications, including digital art creation, and VR/AR. Although several recent works have addressed the inpainting and text-guided content generation problem on NeRFs, they have various limitations in directly generating consistent novel content seamlessly with the background based on the text input. Specifically, [4, 24, 41] allow users to edit the appearance of an existing object in the NeRF based on the text prompt, but their generations are limited by the original target object, thus they cannot handle substantial geometry changes. In [19, 31] a given NeRF is inpainted by removing the target object and inferring the background, but their inpainting task is not generative as they do not match the inferred background with any other user input. Recent generative works [12, 21, 26, 32] produce new 3D or 4D content from the text input, but the generation is not conditioned on the existing background. While [27] can generate contents around the target object without removing it, generative inpainting on NeRF should allow users to remove target objects while filling the exposed region with plausible 3D content, which was previously totally or partially occluded in the given scene. While applying recent diffusion models [22] on generative 2D inpainting may largely solve the problem, simply extending diffusion models to higher dimensions to static or dynamic NeRFs introduces extensive challenges, including new and more complex network structures and sufficient higher-dimensional training data. On the other hand, NeRFs are continuous multiview estimations on its training images which provide a natural connection between 2D images and NeRF, enabling us to propagate the 2D inpainting results by diffusion models to the underlying scene. Thus, in this paper, we propose Inpaint4DNeRF, the first work on text-guided generative NeRF inpainting with diffusion models, which can be naturally extended to inpaint 4D dynamic NeRFs. Given a text prompt, and a target foreground region specified by the user by text or on the single individual image(s), our method first utilizes stable diffusion to inpaint a few independent training views. Next, we regard these inpainted views as seed images and infer a coarse geometry proxy from them. With the strong guidance by seed images and their geometry proxies, the other views being inpainted are constrained to be consistent with the seed images when refined with stable diffusion, which is followed by NeRF finetuning with progressive updates on the training views to obtain final multiview convergence. For dynamic cases, once generative inpainting is achieved on a static single frame, it can be regarded as the seed frame where the edited information can be propagated to other frames to naturally inpaint the scene in 4D without bells and whistles. Our generative Inpaint4DNeRF proposes a text-guided approach for inpainting the relevant background after removing static foreground objects specified by a user-supplied prompt, where multiview consistency is meticulously maintained. To summarize, Inpaint4DNeRF presents the following contributions. First, harnessing the power of recent diffusion models on image inpainting, we can directly generate textguided content with new geometry while being consistent with the context given by the unmasked background. Second, our approach infers and refines other views from initially inpainted seed images to achieve multiview consistency across all the given views, where temporal consistency is naturally enforced and achieved. This paper is available on arxiv under CC 4.0 license. Authors: (1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk); (2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk); (3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk); (4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk); (5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Authors: Authors: (1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk); (2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk); (3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk); (4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk); (5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 2.3. Text-Guided Visual Content Generation 3. Method 3. Method 3.1. Training View Pre-processing 3.1. Training View Pre-processing 3.2. Progressive Training 3.2. Progressive Training 3.3. 4D Extension 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 4.2. Ablation and comparison 5. Conclusion and 6. References 5. Conclusion and 6. References ABSTRACT Current Neural Radiance Fields (NeRF) can generate photorealistic novel views. For editing 3D scenes represented by NeRF, with the advent of generative models, this paper proposes Inpaint4DNeRF to capitalize on state-of-the-art stable diffusion models (e.g., ControlNet [39]) for direct generation of the underlying completed background content, regardless of static or dynamic. The key advantages of this generative approach for NeRF inpainting are twofold. First, after rough mask propagation, to complete or fill in previously occluded content, we can individually generate a small subset of completed images with plausible content, called seed images, from which simple 3D geometry proxies can be derived. Second and the remaining problem is thus 3D multiview consistency among all completed images, now guided by the seed images and their 3D proxies. Without other bells and whistles, our generative Inpaint4DNeRF baseline framework is general which can be readily extended to 4D dynamic NeRFs, where temporal consistency can be naturally handled in a similar way as our multiview consistency Current Neural Radiance Fields (NeRF) can generate photorealistic novel views. For editing 3D scenes represented by NeRF, with the advent of generative models, this paper proposes Inpaint4DNeRF to capitalize on state-of-the-art stable diffusion models (e.g., ControlNet [39]) for direct generation of the underlying completed background content, regardless of static or dynamic. The key advantages of this generative approach for NeRF inpainting are twofold. First, after rough mask propagation, to complete or fill in previously occluded content, we can individually generate a small subset of completed images with plausible content, called seed images, from which simple 3D geometry proxies can be derived. Second and the remaining problem is thus 3D multiview consistency among all completed images, now guided by the seed images and their 3D proxies. Without other bells and whistles, our generative Inpaint4DNeRF baseline framework is general which can be readily extended to 4D dynamic NeRFs, where temporal consistency can be naturally handled in a similar way as our multiview consistency direct 1. INTRODUCTION Recent development of Neural Radiance Fields (NeRF) [18] and its dynamic variants including [20, 25, 28] have shown their great potential in modeling scenes in 3D and 4D. This representation is suitable for scene editing from straightforward user inputs such as text prompts, eliminating the need for modeling and animating in detail. One important task in scene editing is generative inpainting, which refers to generating plausible content that is consistent with the background scene. Generative inpainting has a wide range of potential applications, including digital art creation, and VR/AR. Although several recent works have addressed the inpainting and text-guided content generation problem on NeRFs, they have various limitations in directly generating consistent novel content seamlessly with the background based on the text input. Specifically, [4, 24, 41] allow users to edit the appearance of an existing object in the NeRF based on the text prompt, but their generations are limited by the original target object, thus they cannot handle substantial geometry changes. In [19, 31] a given NeRF is inpainted by removing the target object and inferring the background, but their inpainting task is not generative as they do not match the inferred background with any other user input. Recent generative works [12, 21, 26, 32] produce new 3D or 4D content from the text input, but the generation is not conditioned on the existing background. While [27] can generate contents around the target object without removing it, generative inpainting on NeRF should allow users to remove target objects while filling the exposed region with plausible 3D content, which was previously totally or partially occluded in the given scene. While applying recent diffusion models [22] on generative 2D inpainting may largely solve the problem, simply extending diffusion models to higher dimensions to static or dynamic NeRFs introduces extensive challenges, including new and more complex network structures and sufficient higher-dimensional training data. On the other hand, NeRFs are continuous multiview estimations on its training images which provide a natural connection between 2D images and NeRF, enabling us to propagate the 2D inpainting results by diffusion models to the underlying scene. Thus, in this paper, we propose Inpaint4DNeRF, the first work on text-guided generative NeRF inpainting with diffusion models, which can be naturally extended to inpaint 4D dynamic NeRFs. Given a text prompt, and a target foreground region specified by the user by text or on the single individual image(s), our method first utilizes stable diffusion to inpaint a few independent training views. Next, we regard these inpainted views as seed images and infer a coarse geometry proxy from them. With the strong guidance by seed images and their geometry proxies, the other views being inpainted are constrained to be consistent with the seed images when refined with stable diffusion, which is followed by NeRF finetuning with progressive updates on the training views to obtain final multiview convergence. For dynamic cases, once generative inpainting is achieved on a static single frame, it can be regarded as the seed frame where the edited information can be propagated to other frames to naturally inpaint the scene in 4D without bells and whistles. Our generative Inpaint4DNeRF proposes a text-guided approach for inpainting the relevant background after removing static foreground objects specified by a user-supplied prompt, where multiview consistency is meticulously maintained. To summarize, Inpaint4DNeRF presents the following contributions. First, harnessing the power of recent diffusion models on image inpainting, we can directly generate textguided content with new geometry while being consistent with the context given by the unmasked background. Second, our approach infers and refines other views from initially inpainted seed images to achieve multiview consistency across all the given views, where temporal consistency is naturally enforced and achieved. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

NeRF Editing and Inpainting Techniques: Abstract and Introduction

NeRF Editing and Inpainting Techniques: Conclusion and References

Authors:
(1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk);
(2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk);
(3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk);
(4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk);
(5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Table of Links Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 3. Method 3.1. Training View Pre-processing 3.2. Progressive Training 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 5. Conclusion and 6. References 2. Related Work 2.1. NeRF Editing NeRF Editing, as pioneered by [5, 14, 38], capitalizes on the capabilities of Neural Radiance Fields (NeRFs) to enable intricate manipulations and edits within 3D scenes and objects, including object removal, geometry transformations, and appearance editing. Enabling editing within the implicit density and radiance field has enormous promise across many fields, including virtual reality, augmented reality, content development, and beyond. Nevertheless, these prior works either edit the geometry of already existing contents in NeRF, or edit only the appearance of the scene. None of these works targets at generating novel contents consistent with the underlying NeRF given by the input images to be inpainted. For this task, [41] generates new texture on an existing object, but does not generate new geometry; [27] generates promptable geometrical content around a target object, but the generation is limited near the object and cannot replace the existing object. In our work, we focus on a subtask of NeRF editing, generative inpainting in NeRF. 2.2. Inpainting Techniques The volume-renderable representation and the training mode of NeRF introduce a natural bridge between the represented 3D or 4D scene and its multiview observations. This enables image editing techniques to be applied on NeRF inpainting. Recent advancements in 2D image inpainting have primarily relied on generative models [29], particularly Generative Adversarial Networks (GANs) [36, 37] and Stable Diffusion (SD) models [17, 23]. These models have demonstrated remarkable capabilities in generating visually plausible predictions for missing pixels. Specifically, the capacity to simulate complex data distributions has drawn notable attention to Stable Diffusion [23], an extension of the denoising diffusion probabilistic model that supports inpainting guided by text prompts. This method produces inpainted pixels and samples of superior quality by using controlled diffusion processes. Diffusion models and noise-conditioned score networks are vital to stable diffusion as they interact to optimize the generation process. Researchers have leveraged Stable Diffusion inpainting for various applications, including semantic inpainting, texture synthesis, and realistic object removal. These applications involve understanding contextual information in the image and generating inpaintings that seamlessly integrate with the existing scene. However, most of these works have been limited to synthesis in the image domain and have yet to explicitly consider maintaining fidelity to the 3D structure across various viewpoints. Several prior works [19, 31] addressed the pure NeRF inpainting problem, which removes target objects and infers the background without generating prompt-guided contents. Notably, our study not only goes beyond traditional 2D image inpainting, addressing the challenge of inpainting within 3D and 4D scenes while maintaining view consistency even under perspective changes, but is also promptable in the sense that the inpainted content is matched with the text description while consistent with the underlying 3D or 4D spatiotemporal scene to be inpainted. This paper is available on arxiv under CC 4.0 license. Authors: (1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk); (2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk); (3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk); (4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk); (5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Authors: Authors: (1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk); (2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk); (3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk); (4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk); (5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 2.3. Text-Guided Visual Content Generation 3. Method 3. Method 3.1. Training View Pre-processing 3.1. Training View Pre-processing 3.2. Progressive Training 3.2. Progressive Training 3.3. 4D Extension 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 4.2. Ablation and comparison 5. Conclusion and 6. References 5. Conclusion and 6. References 2. Related Work 2.1. NeRF Editing NeRF Editing, as pioneered by [5, 14, 38], capitalizes on the capabilities of Neural Radiance Fields (NeRFs) to enable intricate manipulations and edits within 3D scenes and objects, including object removal, geometry transformations, and appearance editing. Enabling editing within the implicit density and radiance field has enormous promise across many fields, including virtual reality, augmented reality, content development, and beyond. Nevertheless, these prior works either edit the geometry of already existing contents in NeRF, or edit only the appearance of the scene. None of these works targets at generating novel contents consistent with the underlying NeRF given by the input images to be inpainted. For this task, [41] generates new texture on an existing object, but does not generate new geometry; [27] generates promptable geometrical content around a target object, but the generation is limited near the object and cannot replace the existing object. In our work, we focus on a subtask of NeRF editing, generative inpainting in NeRF. 2.2. Inpainting Techniques The volume-renderable representation and the training mode of NeRF introduce a natural bridge between the represented 3D or 4D scene and its multiview observations. This enables image editing techniques to be applied on NeRF inpainting. Recent advancements in 2D image inpainting have primarily relied on generative models [29], particularly Generative Adversarial Networks (GANs) [36, 37] and Stable Diffusion (SD) models [17, 23]. These models have demonstrated remarkable capabilities in generating visually plausible predictions for missing pixels. Specifically, the capacity to simulate complex data distributions has drawn notable attention to Stable Diffusion [23], an extension of the denoising diffusion probabilistic model that supports inpainting guided by text prompts. This method produces inpainted pixels and samples of superior quality by using controlled diffusion processes. Diffusion models and noise-conditioned score networks are vital to stable diffusion as they interact to optimize the generation process. Researchers have leveraged Stable Diffusion inpainting for various applications, including semantic inpainting, texture synthesis, and realistic object removal. These applications involve understanding contextual information in the image and generating inpaintings that seamlessly integrate with the existing scene. However, most of these works have been limited to synthesis in the image domain and have yet to explicitly consider maintaining fidelity to the 3D structure across various viewpoints. Several prior works [19, 31] addressed the pure NeRF inpainting problem, which removes target objects and infers the background without generating prompt-guided contents. Notably, our study not only goes beyond traditional 2D image inpainting, addressing the challenge of inpainting within 3D and 4D scenes while maintaining view consistency even under perspective changes, but is also promptable in the sense that the inpainted content is matched with the text description while consistent with the underlying 3D or 4D spatiotemporal scene to be inpainted. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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