title: Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic
    Gaussians
summary: Creating high-fidelity 3D head avatars has always been a research hotspot,
  but there remains a great challenge under lightweight sparse view setups. In
  this paper, we propose Gaussian Head Avatar represented by controllable 3D
  Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D
  Gaussians and a fully learned MLP-based deformation field to capture complex
  expressions. The two parts benefit each other, thereby our method can model
  fine-grained dynamic details while ensuring expression accuracy. Furthermore,
  we devise a well-designed geometry-guided initialization strategy based on
  implicit SDF and Deep Marching Tetrahedra for the stability and convergence of
  the training procedure. Experiments show our approach outperforms other
  state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering
  quality at 2K resolution even under exaggerated expressions.
paper_authors._id: 65713d8f974502490bc6a14c
paper_authors.name: Yuelang Xu
paper_authors._id: 65713d8f974502490bc6a14d
paper_authors.name: Benwang Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-07T11:43:00.131Z
paper_authors._id: 63e5efafb6a40bf941da1806
paper_authors.avatarUrl: /avatars/1a73a63bb3fda6dfe326ced338d747e4.svg
paper_authors.fullname: chen ben wang
paper_authors.type: user
paper_authors.user: ben55
paper_authors._id: 65713d8f974502490bc6a14e
paper_authors.name: Zhe Li
paper_authors._id: 65713d8f974502490bc6a14f
paper_authors.name: Hongwen Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-07T11:43:43.648Z
paper_authors._id: 638715a1aaee5cd69dc0c401
paper_authors.avatarUrl: /avatars/f5976b3b02fe86191cb7f59c3dae102b.svg
paper_authors.fullname: Hongwen Zhang
paper_authors.type: user
paper_authors.user: HongwenZhang
paper_authors._id: 65713d8f974502490bc6a150
paper_authors.name: Lizhen Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-07T11:43:36.947Z
paper_authors._id: 63451d2dfeba4bdba59cf9b1
paper_authors.avatarUrl: /avatars/ff5dd2f3b502c54e7c3e2512c3e98b28.svg
paper_authors.fullname: Lizhen Wang
paper_authors.type: user
paper_authors.user: wanglz14
paper_authors._id: 65713d8f974502490bc6a151
paper_authors.name: Zerong Zheng
paper_authors._id: 65713d8f974502490bc6a152
paper_authors.name: Yebin Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-07T11:43:22.316Z
paper_authors._id: 62e14dbe4db2175cd2735a80
paper_authors.avatarUrl: /avatars/e6385dcedcb97e1b36281b49210321aa.svg
paper_authors.fullname: Yebin Liu
paper_authors.type: user
paper_authors.user: YebinLiu
paper_title: Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic
    Gaussians
paper_summary: Creating high-fidelity 3D head avatars has always been a research hotspot,
  but there remains a great challenge under lightweight sparse view setups. In
  this paper, we propose Gaussian Head Avatar represented by controllable 3D
  Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D
  Gaussians and a fully learned MLP-based deformation field to capture complex
  expressions. The two parts benefit each other, thereby our method can model
  fine-grained dynamic details while ensuring expression accuracy. Furthermore,
  we devise a well-designed geometry-guided initialization strategy based on
  implicit SDF and Deep Marching Tetrahedra for the stability and convergence of
  the training procedure. Experiments show our approach outperforms other
  state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering
  quality at 2K resolution even under exaggerated expressions.
paper_ai_keywords: Gaussian Head Avatar 3D Gaussians MLP-based deformation field expression accuracy geometry-guided initialization strategy implicit SDF Deep Marching Tetrahedra ultra high-fidelity rendering sparse-view methods