  In this paper, we present a diffusion model-based framework for animating
people from a single image for a given target 3D motion sequence. Our approach
has two core components: a) learning priors about invisible parts of the human
body and clothing, and b) rendering novel body poses with proper clothing and
texture. For the first part, we learn an in-filling diffusion model to
hallucinate unseen parts of a person given a single image. We train this model
on texture map space, which makes it more sample-efficient since it is
invariant to pose and viewpoint. Second, we develop a diffusion-based rendering
pipeline, which is controlled by 3D human poses. This produces realistic
renderings of novel poses of the person, including clothing, hair, and
plausible in-filling of unseen regions. This disentangled approach allows our
method to generate a sequence of images that are faithful to the target motion
in the 3D pose and, to the input image in terms of visual similarity. In
addition to that, the 3D control allows various synthetic camera trajectories
to render a person. Our experiments show that our method is resilient in
generating prolonged motions and varied challenging and complex poses compared
to prior methods. Please check our website for more details:
https://boyiliee.github.io/3DHM.github.io/.
