AvatarVerse: High-quality & Stable 3D Avatar Creation from Text and Pose
Creating expressive, diverse and high-quality 3D avatars from highly
customized text descriptions and pose guidance is a challenging task, due to
the intricacy of modeling and texturing in 3D that ensure details and various
styles (realistic, fictional, etc). We present AvatarVerse, a stable pipeline
for generating expressive high-quality 3D avatars from nothing but text
descriptions and pose guidance. In specific, we introduce a 2D diffusion model
conditioned on DensePose signal to establish 3D pose control of avatars through
2D images, which enhances view consistency from partially observed scenarios.
It addresses the infamous Janus Problem and significantly stablizes the
generation process. Moreover, we propose a progressive high-resolution 3D
synthesis strategy, which obtains substantial improvement over the quality of
the created 3D avatars. To this end, the proposed AvatarVerse pipeline achieves
zero-shot 3D modeling of 3D avatars that are not only more expressive, but also
in higher quality and fidelity than previous works. Rigorous qualitative
evaluations and user studies showcase AvatarVerse's superiority in synthesizing
high-fidelity 3D avatars, leading to a new standard in high-quality and stable
3D avatar creation. Our project page is: https://avatarverse3d.github.io
64d1d0429cff738203befffb
Huichao Zhang
claimed_verified
2023-08-09T00:42:55.538Z
636241c6b64a56694266509f
/avatars/90bf6156b649b78db13f08d0770951ad.svg
zhanghuichao
user
liqingzju
64d1d0429cff738203befffc
Bowen Chen
claimed_verified
2023-08-08T14:33:57.343Z
6361dd166945df7441b893fa
/avatars/b3ae6888a41aab8c2a7ef9f7320565c4.svg
Bowen Chen 
user
chenbowen
64d1d0429cff738203befffd
Hao Yang
claimed_verified
2023-08-10T01:13:53.951Z
6322d55796d6c8518eaff8c5
/avatars/7309e77145674d01dbd842b0dcda171f.svg
Hao Yang
user
hayaalex
64d1d0429cff738203befffe
Liao Qu
claimed_verified
2023-08-08T13:44:33.486Z
64b796079ebb7e6c7ddcdabf
/avatars/51af43cab078705b8745b4f942f542e5.svg
Liao Qu
user
leo1117
64d1d0429cff738203beffff
Xu Wang
64d1d0429cff738203bf0000
Li Chen
64d1d0429cff738203bf0001
Chao Long
64d1d0429cff738203bf0002
Feida Zhu
64d1d0429cff738203bf0003
Kang Du
64d1d0429cff738203bf0004
Min Zheng
AvatarVerse: High-quality & Stable 3D Avatar Creation from Text and Pose
Creating expressive, diverse and high-quality 3D avatars from highly
customized text descriptions and pose guidance is a challenging task, due to
the intricacy of modeling and texturing in 3D that ensure details and various
styles (realistic, fictional, etc). We present AvatarVerse, a stable pipeline
for generating expressive high-quality 3D avatars from nothing but text
descriptions and pose guidance. In specific, we introduce a 2D diffusion model
conditioned on DensePose signal to establish 3D pose control of avatars through
2D images, which enhances view consistency from partially observed scenarios.
It addresses the infamous Janus Problem and significantly stablizes the
generation process. Moreover, we propose a progressive high-resolution 3D
synthesis strategy, which obtains substantial improvement over the quality of
the created 3D avatars. To this end, the proposed AvatarVerse pipeline achieves
zero-shot 3D modeling of 3D avatars that are not only more expressive, but also
in higher quality and fidelity than previous works. Rigorous qualitative
evaluations and user studies showcase AvatarVerse's superiority in synthesizing
high-fidelity 3D avatars, leading to a new standard in high-quality and stable
3D avatar creation. Our project page is: https://avatarverse3d.github.io
AvatarVerse generates high-fidelity 3D avatars from text descriptions and pose guidance using a 2D diffusion model and progressive high-resolution synthesis.
2D diffusion model DensePose signal 3D pose control view consistency Janus Problem progressive high-resolution 3D synthesis zero-shot 3D modeling high-fidelity 3D avatars