  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 in-the-wild
dataset. Project page: https://fudan-generative-vision.github.io/champ.
