Champ: Controllable and Consistent Human Image Animation with 3D
  Parametric Guidance
In this study, we introduce a methodology for human image animation by
leveraging a 3D human parametric model within a latent diffusion framework to
enhance shape alignment and motion guidance in curernt human generative
techniques. The methodology utilizes the SMPL(Skinned Multi-Person Linear)
model as the 3D human parametric model to establish a unified representation of
body shape and pose. This facilitates the accurate capture of intricate human
geometry and motion characteristics from source videos. Specifically, we
incorporate rendered depth images, normal maps, and semantic maps obtained from
SMPL sequences, alongside skeleton-based motion guidance, to enrich the
conditions to the latent diffusion model with comprehensive 3D shape and
detailed pose attributes. A multi-layer motion fusion module, integrating
self-attention mechanisms, is employed to fuse the shape and motion latent
representations in the spatial domain. By representing the 3D human parametric
model as the motion guidance, we can perform parametric shape alignment of the
human body between the reference image and the source video motion.
Experimental evaluations conducted on benchmark datasets demonstrate the
methodology's superior ability to generate high-quality human animations that
accurately capture both pose and shape variations. Furthermore, our approach
also exhibits superior generalization capabilities on the proposed wild
dataset. Project page: https://fudan-generative-vision.github.io/champ.
660107379e1cf5eb41e51cfd
Shenhao Zhu
admin_assigned
2024-03-25T10:16:18.584Z
660022fd7a3a9bbfcd4f27ee
/avatars/6d6472a52c6d5ddc7d9a8df62d70e567.svg
Shenhao Zhu
user
ShenhaoZhu
660107379e1cf5eb41e51cfe
Junming Leo Chen
extracted_confirmed
2024-03-25T05:20:33.398Z
65d802be7eed9113f6f3e20b
https://cdn-avatars.huggingface.co/v1/production/uploads/65d802be7eed9113f6f3e20b/TdjqI_GBn3Bc9bvxgqnDF.jpeg
Junming Leo Chen
user
Leooo333
660107379e1cf5eb41e51cff
Zuozhuo Dai
admin_assigned
2024-03-25T10:16:30.544Z
6316fe2a6d322c52490f90e8
/avatars/8c896b3733e183657009a426834a9dab.svg
Dai Zuozhuo
user
zyand
660107379e1cf5eb41e51d00
Yinghui Xu
660107379e1cf5eb41e51d01
Xun Cao
660107379e1cf5eb41e51d02
Yao Yao
660107379e1cf5eb41e51d03
Hao Zhu
660107379e1cf5eb41e51d04
Siyu Zhu
Champ: Controllable and Consistent Human Image Animation with 3D
  Parametric Guidance
In this study, we introduce a methodology for human image animation by
leveraging a 3D human parametric model within a latent diffusion framework to
enhance shape alignment and motion guidance in curernt human generative
techniques. The methodology utilizes the SMPL(Skinned Multi-Person Linear)
model as the 3D human parametric model to establish a unified representation of
body shape and pose. This facilitates the accurate capture of intricate human
geometry and motion characteristics from source videos. Specifically, we
incorporate rendered depth images, normal maps, and semantic maps obtained from
SMPL sequences, alongside skeleton-based motion guidance, to enrich the
conditions to the latent diffusion model with comprehensive 3D shape and
detailed pose attributes. A multi-layer motion fusion module, integrating
self-attention mechanisms, is employed to fuse the shape and motion latent
representations in the spatial domain. By representing the 3D human parametric
model as the motion guidance, we can perform parametric shape alignment of the
human body between the reference image and the source video motion.
Experimental evaluations conducted on benchmark datasets demonstrate the
methodology's superior ability to generate high-quality human animations that
accurately capture both pose and shape variations. Furthermore, our approach
also exhibits superior generalization capabilities on the proposed wild
dataset. Project page: https://fudan-generative-vision.github.io/champ.
A method for human image animation using a 3D human parametric model within a latent diffusion framework enhances shape alignment and motion guidance by integrating depth images, normal maps, and semantic maps with self-attention mechanisms.
3D human parametric model latent diffusion framework SMPL model depth images normal maps semantic maps skeleton-based motion guidance multi-layer motion fusion module self-attention mechanisms parametric shape alignment benchmark datasets wild dataset