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Controllability, generalizability and efficiency are the major objectives of constructing face avatars represented by neural implicit field.
Display of surfaces from volume data
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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The unreasonable effectiveness of deep features as a perceptual metric
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Talking face generation by adversarially disentangled audio-visual representation
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Stylegan of all trades: Image manipulation with only pretrained stylegan
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Motion representations for articulated animation
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Gan prior embedded network for blind face restoration in the wild
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pixelnerf: Neural radiance fields from one or few images
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2021
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Flow-guided one-shot talking face generation with a high-resolution audio-visual dataset
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Pose-controllable talking face generation by implicitly modularized audio-visual representation
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Efficient geometry-aware 3D generative adversarial networks
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KeypointNeRF: Generalizing image-based volumetric avatars using relative spatial encoding of keypoints
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Multiface: A dataset for neural face rendering
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Gan inversion: A survey
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Styleheat: One-shot high-resolution editable talking face generation via pretrained stylegan
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Generalized one-shot domain adaption of generative adversarial networks
Zicheng Zhang, Yinglu Liu, Congying Han, Tiande Guo, Ting Yao, and Tao Mei · 2022
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I M Avatar: Implicit morphable head avatars from videos
Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C. Bühler, Xu Chen, Michael J. Black, and Otmar Hilliges · 2022
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