title: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors
summary: 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.
paper_authors._id: 66bacd9ec9b2ab14b3988f49
paper_authors.name: Xiaozheng Zheng
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/fbc429db27443fb47769167f0eb3fd9e.svg
paper_authors.fullname: Xiaozheng Zheng
paper_authors.type: user
paper_authors.user: zxz267
paper_authors._id: 66bacd9ec9b2ab14b3988f4a
paper_authors.name: Chao Wen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-13T07:38:14.286Z
paper_authors._id: 650d0c442a602ba349183fca
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/650d0c442a602ba349183fca/-UId-lPK64nRXjGxPJE9k.png
paper_authors.fullname: Chao Wen
paper_authors.type: user
paper_authors.user: walsvid
paper_authors._id: 66bacd9ec9b2ab14b3988f4b
paper_authors.name: Zhaohu Li
paper_authors.status: admin_assigned
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paper_authors._id: 66a31d9e9b39142573bfbb55
paper_authors.avatarUrl: /avatars/2835e50156b9ae5cdfdc8514e6a102d0.svg
paper_authors.fullname: zhaohu li
paper_authors.type: user
paper_authors.user: zhaohu2
paper_authors._id: 66bacd9ec9b2ab14b3988f4c
paper_authors.name: Weiyi Zhang
paper_authors._id: 66bacd9ec9b2ab14b3988f4d
paper_authors.name: Zhuo Su
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-13T09:16:26.838Z
paper_authors._id: 655238b2957bf8a398eca2a1
paper_authors.avatarUrl: /avatars/f0e5af3de113b998bcf84953ecd8b930.svg
paper_authors.fullname: Zhuo Su
paper_authors.type: user
paper_authors.user: hellozhuo
paper_authors._id: 66bacd9ec9b2ab14b3988f4e
paper_authors.name: Xu Chang
paper_authors._id: 66bacd9ec9b2ab14b3988f4f
paper_authors.name: Yang Zhao
paper_authors._id: 66bacd9ec9b2ab14b3988f50
paper_authors.name: Zheng Lv
paper_authors._id: 66bacd9ec9b2ab14b3988f51
paper_authors.name: Xiaoyuan Zhang
paper_authors._id: 66bacd9ec9b2ab14b3988f52
paper_authors.name: Yongjie Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-13T09:17:34.434Z
paper_authors._id: 64c62590ed521f27a41f9338
paper_authors.avatarUrl: /avatars/0e849359c0acac44d2513775cb6d3300.svg
paper_authors.fullname: Zhang Yongjie
paper_authors.type: user
paper_authors.user: yongjie-zhang-mail
paper_authors._id: 66bacd9ec9b2ab14b3988f53
paper_authors.name: Guidong Wang
paper_authors._id: 66bacd9ec9b2ab14b3988f54
paper_authors.name: Lan Xu
paper_title: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors
paper_summary: 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.
paper_ai_summary: A framework for creating 3D photorealistic and animatable head avatars using prior learning and few-shot personalization with Gaussian Splatting and dynamic modeling.
paper_ai_keywords: 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