  Learning 3D human pose prior is essential to human-centered AI. Here, we
present GFPose, a versatile framework to model plausible 3D human poses for
various applications. At the core of GFPose is a time-dependent score network,
which estimates the gradient on each body joint and progressively denoises the
perturbed 3D human pose to match a given task specification. During the
denoising process, GFPose implicitly incorporates pose priors in gradients and
unifies various discriminative and generative tasks in an elegant framework.
Despite the simplicity, GFPose demonstrates great potential in several
downstream tasks. Our experiments empirically show that 1) as a
multi-hypothesis pose estimator, GFPose outperforms existing SOTAs by 20% on
Human3.6M dataset. 2) as a single-hypothesis pose estimator, GFPose achieves
comparable results to deterministic SOTAs, even with a vanilla backbone. 3)
GFPose is able to produce diverse and realistic samples in pose denoising,
completion and generation tasks. Project page
https://sites.google.com/view/gfpose/
