Authors:
(1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.;
(2) Jian Ren, Snap Inc. with Corresponding author: jren@snapchat.com;
(3) Aliaksandr Siarohin, Snap Inc.;
(4) Ivan Skorokhodov, Snap Inc.;
(5) Yanyu Li, Snap Inc.;
(6) Dahua Lin, CUHK;
(7) Xihui Liu, HKU;
(8) Ziwei Liu, NTU;
(9) Sergey Tulyakov, Snap Inc. Table of Links Abstract and 1 Introduction 2 Related Work 3 Our Approach and 3.1 Preliminaries and Problem Setting 3.2 Latent Structural Diffusion Model 3.3 Structure-Guided Refiner 4 Human Verse Dataset 5 Experiments 5.1 Main Results 5.2 Ablation Study 6 Discussion and References A Appendix and A.1 Additional Quantitative Results A.2 More Implementation Details and A.3 More Ablation Study Results A.4 More User Study Details A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration A.7 More Comparison Results and A.8 Additional Qualitative Results A.9 Licenses A.5 IMPACT OF RANDOM SEED AND MODEL ROBUSTNESS To further validate our model’s robustness to the impact of random seed, we inference with the same input conditions (i.e., text prompt and pose skeleton) and use different random seeds for generation. The results are shown in Fig. 5, which suggest that our proposed framework is robust to generate high-quality and text-aligned human images over multiple arbitrary random seeds. A.6 BOARDER IMPACT AND ETHICAL CONSIDERATION Generating realistic humans benefits a wide range of applications. It enriches creative domains such as art, design, and entertainment by enabling the creation of highly realistic and emotionally resonant visuals (Liu et al., 2022a;b). Besides, it streamlines design processes, reducing time and resources needed for tasks like graphic design and content production. However, it could be misused for malicious purposes like deepfake or forgery generation. We believe that the proper use of this technique will enhance the machine learning research and digital entertainment. We also advocate all the generated images should be labeled as “synthetic” to avoid negative social impacts. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.; (2) Jian Ren, Snap Inc. with Corresponding author: jren@snapchat.com; (3) Aliaksandr Siarohin, Snap Inc.; (4) Ivan Skorokhodov, Snap Inc.; (5) Yanyu Li, Snap Inc.; (6) Dahua Lin, CUHK; (7) Xihui Liu, HKU; (8) Ziwei Liu, NTU; (9) Sergey Tulyakov, Snap Inc. Authors: Authors: (1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.; (2) Jian Ren, Snap Inc. with Corresponding author: jren@snapchat.com; (3) Aliaksandr Siarohin, Snap Inc.; (4) Ivan Skorokhodov, Snap Inc.; (5) Yanyu Li, Snap Inc.; (6) Dahua Lin, CUHK; (7) Xihui Liu, HKU; (8) Ziwei Liu, NTU; (9) Sergey Tulyakov, Snap Inc. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Our Approach and 3.1 Preliminaries and Problem Setting 3 Our Approach and 3.1 Preliminaries and Problem Setting 3.2 Latent Structural Diffusion Model 3.2 Latent Structural Diffusion Model 3.3 Structure-Guided Refiner 3.3 Structure-Guided Refiner 4 Human Verse Dataset 4 Human Verse Dataset 5 Experiments 5 Experiments 5.1 Main Results 5.1 Main Results 5.2 Ablation Study 5.2 Ablation Study 6 Discussion and References 6 Discussion and References A Appendix and A.1 Additional Quantitative Results A Appendix and A.1 Additional Quantitative Results A.2 More Implementation Details and A.3 More Ablation Study Results A.2 More Implementation Details and A.3 More Ablation Study Results A.4 More User Study Details A.4 More User Study Details A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration A.7 More Comparison Results and A.8 Additional Qualitative Results A.7 More Comparison Results and A.8 Additional Qualitative Results A.9 Licenses A.9 Licenses A.5 IMPACT OF RANDOM SEED AND MODEL ROBUSTNESS To further validate our model’s robustness to the impact of random seed, we inference with the same input conditions (i.e., text prompt and pose skeleton) and use different random seeds for generation. The results are shown in Fig. 5, which suggest that our proposed framework is robust to generate high-quality and text-aligned human images over multiple arbitrary random seeds. A.6 BOARDER IMPACT AND ETHICAL CONSIDERATION Generating realistic humans benefits a wide range of applications. It enriches creative domains such as art, design, and entertainment by enabling the creation of highly realistic and emotionally resonant visuals (Liu et al., 2022a;b). Besides, it streamlines design processes, reducing time and resources needed for tasks like graphic design and content production. However, it could be misused for malicious purposes like deepfake or forgery generation. We believe that the proper use of this technique will enhance the machine learning research and digital entertainment. We also advocate all the generated images should be labeled as “synthetic” to avoid negative social impacts. 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

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Impact of Random Seed and Model Robustness on High-Quality Human Image Generation

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