A generic diffusion-based approach for 3D human pose prediction in the wild

3D human pose forecasting, i.e., predicting a sequence of future human 3D poses given a sequence of past observed ones, is a challenging spatio-temporal task. It can be more challenging in real-world applications where occlusions will inevitably happen, and estimated 3D coordinates of joints would contain some noise. We provide a unified formulation in which incomplete elements (no matter in the prediction or observation) are treated as noise and propose a conditional diffusion model that denoises them and forecasts plausible poses. Instead of naively predicting all future frames at once, our model consists of two cascaded sub-models, each specialized for modeling short and long horizon distributions. We also propose a generic framework to improve any 3D pose forecasting model by leveraging our diffusion model in two additional steps: a pre-processing step to repair the inputs and a post-processing step to refine the outputs. We investigate our findings on four standard datasets (Human3.6M, HumanEva-I, AMASS, and 3DPW) and obtain significant improvements over the state-of-the-art. The code will be made available online.