GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning
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.
6581359b8108945fd3c61654
Ye Yuan
6581359b8108945fd3c61655
Xueting Li
admin_assigned
2023-12-19T09:30:02.457Z
6263685063f73be3d2f97604
/avatars/9c5d2f81cfd84bc835e4feb11b5e60b3.svg
Xueting Li
user
sunshineatnoon
6581359b8108945fd3c61656
Yangyi Huang
6581359b8108945fd3c61657
Shalini De Mello
admin_assigned
2023-12-19T09:30:24.424Z
623f6ac3f1c25923c98a892a
https://cdn-avatars.huggingface.co/v1/production/uploads/1648323201428-noauth.png
Shalini De Mello
user
shalinidemello
6581359b8108945fd3c61658
Koki Nagano
admin_assigned
2023-12-19T09:30:32.186Z
637eb1c71dbae09191059243
/avatars/674b1a6e419c758a8613338d20f21544.svg
Koki Nagano
user
luminohope
6581359b8108945fd3c61659
Jan Kautz
6581359b8108945fd3c6165a
Umar Iqbal
GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning
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.
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.
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