title: GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar
    Editor
summary: We introduce GaussianAvatar-Editor, an innovative framework for text-driven
  editing of animatable Gaussian head avatars that can be fully controlled in
  expression, pose, and viewpoint. Unlike static 3D Gaussian editing, editing
  animatable 4D Gaussian avatars presents challenges related to motion occlusion
  and spatial-temporal inconsistency. To address these issues, we propose the
  Weighted Alpha Blending Equation (WABE). This function enhances the blending
  weight of visible Gaussians while suppressing the influence on non-visible
  Gaussians, effectively handling motion occlusion during editing. Furthermore,
  to improve editing quality and ensure 4D consistency, we incorporate
  conditional adversarial learning into the editing process. This strategy helps
  to refine the edited results and maintain consistency throughout the animation.
  By integrating these methods, our GaussianAvatar-Editor achieves photorealistic
  and consistent results in animatable 4D Gaussian editing. We conduct
  comprehensive experiments across various subjects to validate the effectiveness
  of our proposed techniques, which demonstrates the superiority of our approach
  over existing methods. More results and code are available at: [Project
  Link](https://xiangyueliu.github.io/GaussianAvatar-Editor/).
paper_authors._id: 678dcddd1c0169c0bc73364e
paper_authors.name: Xiangyue Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-21T13:10:10.397Z
paper_authors._id: 64edbc604f6a405a135ee506
paper_authors.avatarUrl: /avatars/9f4976d8bc093cb8c5604b7235e980e4.svg
paper_authors.fullname: Xiangyue Liu
paper_authors.type: user
paper_authors.user: Liuxy-cx
paper_authors._id: 678dcddd1c0169c0bc73364f
paper_authors.name: Kunming Luo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-20T14:37:28.443Z
paper_authors._id: 64baa2a530a1f0f0f03e758c
paper_authors.avatarUrl: /avatars/100deaba962f49520a028c94b51720b1.svg
paper_authors.fullname: Kunming Luo
paper_authors.type: user
paper_authors.user: coolbeam
paper_authors._id: 678dcddd1c0169c0bc733650
paper_authors.name: Heng Li
paper_authors._id: 678dcddd1c0169c0bc733651
paper_authors.name: Qi Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-21T10:07:10.035Z
paper_authors._id: 65001a3632d2159207e9af77
paper_authors.avatarUrl: /avatars/4b7bca2bdc9216fc06857dc1c2183ae6.svg
paper_authors.fullname: Qiqi Meng
paper_authors.type: user
paper_authors.user: mayushii
paper_authors._id: 678dcddd1c0169c0bc733652
paper_authors.name: Yuan Liu
paper_authors._id: 678dcddd1c0169c0bc733653
paper_authors.name: Li Yi
paper_authors._id: 678dcddd1c0169c0bc733654
paper_authors.name: Ping Tan
paper_title: GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar
    Editor
paper_summary: We introduce GaussianAvatar-Editor, an innovative framework for text-driven
  editing of animatable Gaussian head avatars that can be fully controlled in
  expression, pose, and viewpoint. Unlike static 3D Gaussian editing, editing
  animatable 4D Gaussian avatars presents challenges related to motion occlusion
  and spatial-temporal inconsistency. To address these issues, we propose the
  Weighted Alpha Blending Equation (WABE). This function enhances the blending
  weight of visible Gaussians while suppressing the influence on non-visible
  Gaussians, effectively handling motion occlusion during editing. Furthermore,
  to improve editing quality and ensure 4D consistency, we incorporate
  conditional adversarial learning into the editing process. This strategy helps
  to refine the edited results and maintain consistency throughout the animation.
  By integrating these methods, our GaussianAvatar-Editor achieves photorealistic
  and consistent results in animatable 4D Gaussian editing. We conduct
  comprehensive experiments across various subjects to validate the effectiveness
  of our proposed techniques, which demonstrates the superiority of our approach
  over existing methods. More results and code are available at: [Project
  Link](https://xiangyueliu.github.io/GaussianAvatar-Editor/).
paper_ai_keywords: GaussianAvatar-Editor text-driven editing animatable Gaussian head avatars expression control pose control viewpoint control static 3D Gaussian editing motion occlusion spatial-temporal inconsistency Weighted Alpha Blending Equation (WABE) conditional adversarial learning photorealistic editing 4D Gaussian editing