How HyperHuman Pushes the Boundaries of Realistic Human Image Generation

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 6 DISCUSSION Conclusion. In this paper, we propose a novel framework HyperHuman to generate in-the-wild human images of high quality. To enforce the joint learning of image appearance, spatial relationship, and geometry in a unified network, we propose Latent Structural Diffusion Model that simultaneously denoises the depth and normal along with RGB. Then we devise Structure-Guided Refiner to compose the predicted conditions for detailed generation. Extensive experiments demonstrate that our framework yields superior performance, generating realistic humans under diverse scenarios. Limitation and Future Work. As an early attempt in human generation foundation model, our approach creates controllable human of high realism. However, due to the limited performance of existing pose/depth/normal estimators for in-the-wild humans, we find it sometimes fails to generate subtle details like finger and eyes. 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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 6 DISCUSSION Conclusion. In this paper, we propose a novel framework HyperHuman to generate in-the-wild human images of high quality. To enforce the joint learning of image appearance, spatial relationship, and geometry in a unified network, we propose Latent Structural Diffusion Model that simultaneously denoises the depth and normal along with RGB. Then we devise Structure-Guided Refiner to compose the predicted conditions for detailed generation. Extensive experiments demonstrate that our framework yields superior performance, generating realistic humans under diverse scenarios. HyperHuman Limitation and Future Work. As an early attempt in human generation foundation model, our approach creates controllable human of high realism. However, due to the limited performance of existing pose/depth/normal estimators for in-the-wild humans, we find it sometimes fails to generate subtle details like finger and eyes. Besides, the current pipeline still requires body skeleton as input, where deep priors like LLMs can be explored to achieve text-to-pose generation in future work. REFERENCES Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, et al. ediffi: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324, 2022. 3 Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, and Jun Zhu. One transformer fits all distributions in multi-modal diffusion at scale. arXiv preprint arXiv:2303.06555, 2023. 6 Mikołaj Binkowski, Danica J Sutherland, Michael Arbel, and Arthur Gretton. Demystifying mmd ´ gans. arXiv preprint arXiv:1801.01401, 2018. 14 Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, and Saehoon Kim. 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