HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors
In this paper, we present a novel 3D head avatar creation approach capable of
generalizing from few-shot in-the-wild data with high-fidelity and animatable
robustness. Given the underconstrained nature of this problem, incorporating
prior knowledge is essential. Therefore, we propose a framework comprising
prior learning and avatar creation phases. The prior learning phase leverages
3D head priors derived from a large-scale multi-view dynamic dataset, and the
avatar creation phase applies these priors for few-shot personalization. Our
approach effectively captures these priors by utilizing a Gaussian
Splatting-based auto-decoder network with part-based dynamic modeling. Our
method employs identity-shared encoding with personalized latent codes for
individual identities to learn the attributes of Gaussian primitives. During
the avatar creation phase, we achieve fast head avatar personalization by
leveraging inversion and fine-tuning strategies. Extensive experiments
demonstrate that our model effectively exploits head priors and successfully
generalizes them to few-shot personalization, achieving photo-realistic
rendering quality, multi-view consistency, and stable animation.
66bacd9ec9b2ab14b3988f49
Xiaozheng Zheng
claimed_verified
2024-08-13T10:04:14.062Z
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Xiaozheng Zheng
user
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66bacd9ec9b2ab14b3988f4a
Chao Wen
claimed_verified
2024-08-13T07:38:14.286Z
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Chao Wen
user
walsvid
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Zhaohu Li
admin_assigned
2024-08-13T09:16:03.604Z
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zhaohu li
user
zhaohu2
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Weiyi Zhang
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Zhuo Su
admin_assigned
2024-08-13T09:16:26.838Z
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Zhuo Su
user
hellozhuo
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Xu Chang
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Yang Zhao
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Zheng Lv
66bacd9ec9b2ab14b3988f51
Xiaoyuan Zhang
66bacd9ec9b2ab14b3988f52
Yongjie Zhang
admin_assigned
2024-08-13T09:17:34.434Z
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Zhang Yongjie
user
yongjie-zhang-mail
66bacd9ec9b2ab14b3988f53
Guidong Wang
66bacd9ec9b2ab14b3988f54
Lan Xu
HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors
In this paper, we present a novel 3D head avatar creation approach capable of
generalizing from few-shot in-the-wild data with high-fidelity and animatable
robustness. Given the underconstrained nature of this problem, incorporating
prior knowledge is essential. Therefore, we propose a framework comprising
prior learning and avatar creation phases. The prior learning phase leverages
3D head priors derived from a large-scale multi-view dynamic dataset, and the
avatar creation phase applies these priors for few-shot personalization. Our
approach effectively captures these priors by utilizing a Gaussian
Splatting-based auto-decoder network with part-based dynamic modeling. Our
method employs identity-shared encoding with personalized latent codes for
individual identities to learn the attributes of Gaussian primitives. During
the avatar creation phase, we achieve fast head avatar personalization by
leveraging inversion and fine-tuning strategies. Extensive experiments
demonstrate that our model effectively exploits head priors and successfully
generalizes them to few-shot personalization, achieving photo-realistic
rendering quality, multi-view consistency, and stable animation.
A framework for creating 3D photorealistic and animatable head avatars using prior learning and few-shot personalization with Gaussian Splatting and dynamic modeling.
Gaussian Splatting auto-decoder network part-based dynamic modeling personalized latent codes prior learning inversion fine-tuning multi-view consistency photo-realistic rendering stable animation