title: GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning
summary: Gaussian splatting has emerged as a powerful 3D representation that harnesses
  the advantages of both explicit (mesh) and implicit (NeRF) 3D representations.
  In this paper, we seek to leverage Gaussian splatting to generate realistic
  animatable avatars from textual descriptions, addressing the limitations (e.g.,
  flexibility and efficiency) imposed by mesh or NeRF-based representations.
  However, a naive application of Gaussian splatting cannot generate high-quality
  animatable avatars and suffers from learning instability; it also cannot
  capture fine avatar geometries and often leads to degenerate body parts. To
  tackle these problems, we first propose a primitive-based 3D Gaussian
  representation where Gaussians are defined inside pose-driven primitives to
  facilitate animation. Second, to stabilize and amortize the learning of
  millions of Gaussians, we propose to use neural implicit fields to predict the
  Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries
  and extract detailed meshes, we propose a novel SDF-based implicit mesh
  learning approach for 3D Gaussians that regularizes the underlying geometries
  and extracts highly detailed textured meshes. Our proposed method, GAvatar,
  enables the large-scale generation of diverse animatable avatars using only
  text prompts. GAvatar significantly surpasses existing methods in terms of both
  appearance and geometry quality, and achieves extremely fast rendering (100
  fps) at 1K resolution.
paper_authors._id: 6581359b8108945fd3c61654
paper_authors.name: Ye Yuan
paper_authors._id: 6581359b8108945fd3c61655
paper_authors.name: Xueting Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-19T09:30:02.457Z
paper_authors._id: 6263685063f73be3d2f97604
paper_authors.avatarUrl: /avatars/9c5d2f81cfd84bc835e4feb11b5e60b3.svg
paper_authors.fullname: Xueting Li
paper_authors.type: user
paper_authors.user: sunshineatnoon
paper_authors._id: 6581359b8108945fd3c61656
paper_authors.name: Yangyi Huang
paper_authors._id: 6581359b8108945fd3c61657
paper_authors.name: Shalini De Mello
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-19T09:30:24.424Z
paper_authors._id: 623f6ac3f1c25923c98a892a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1648323201428-noauth.png
paper_authors.fullname: Shalini De Mello
paper_authors.type: user
paper_authors.user: shalinidemello
paper_authors._id: 6581359b8108945fd3c61658
paper_authors.name: Koki Nagano
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-19T09:30:32.186Z
paper_authors._id: 637eb1c71dbae09191059243
paper_authors.avatarUrl: /avatars/674b1a6e419c758a8613338d20f21544.svg
paper_authors.fullname: Koki Nagano
paper_authors.type: user
paper_authors.user: luminohope
paper_authors._id: 6581359b8108945fd3c61659
paper_authors.name: Jan Kautz
paper_authors._id: 6581359b8108945fd3c6165a
paper_authors.name: Umar Iqbal
paper_title: GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning
paper_summary: Gaussian splatting has emerged as a powerful 3D representation that harnesses
  the advantages of both explicit (mesh) and implicit (NeRF) 3D representations.
  In this paper, we seek to leverage Gaussian splatting to generate realistic
  animatable avatars from textual descriptions, addressing the limitations (e.g.,
  flexibility and efficiency) imposed by mesh or NeRF-based representations.
  However, a naive application of Gaussian splatting cannot generate high-quality
  animatable avatars and suffers from learning instability; it also cannot
  capture fine avatar geometries and often leads to degenerate body parts. To
  tackle these problems, we first propose a primitive-based 3D Gaussian
  representation where Gaussians are defined inside pose-driven primitives to
  facilitate animation. Second, to stabilize and amortize the learning of
  millions of Gaussians, we propose to use neural implicit fields to predict the
  Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries
  and extract detailed meshes, we propose a novel SDF-based implicit mesh
  learning approach for 3D Gaussians that regularizes the underlying geometries
  and extracts highly detailed textured meshes. Our proposed method, GAvatar,
  enables the large-scale generation of diverse animatable avatars using only
  text prompts. GAvatar significantly surpasses existing methods in terms of both
  appearance and geometry quality, and achieves extremely fast rendering (100
  fps) at 1K resolution.
paper_ai_summary: A method combining Gaussian splatting with neural implicit fields and SDF-based mesh learning for generating realistic and animatable avatars from text prompts, achieving high-quality appearance and geometry with fast rendering.
paper_ai_keywords: Gaussian splatting explicit representations implicit representations NeRF primitive-based 3D Gaussian representation neural implicit fields SDF-based mesh learning appearance quality geometry quality fast rendering