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Predicting 3D human poses in real-world scenarios, also known as human poseforecasting, is inevitably subject to noisy inputs arising from inaccurate 3Dpose estimations and occlusions. To address these challenges, we propose adiffusion-based approach that can predict given noisy observations. We framethe prediction task as a denoising problem, where both observation andprediction are considered as a single sequence containing missing elements(whether in the observation or prediction horizon). All missing elements aretreated as noise and denoised with our conditional diffusion model. To betterhandle long-term forecasting horizon, we present a temporal cascaded diffusionmodel. We demonstrate the benefits of our approach on four publicly availabledatasets (Human3.6M, HumanEva-I, AMASS, and 3DPW), outperforming thestate-of-the-art. Additionally, we show that our framework is generic enough toimprove any 3D pose prediction model as a pre-processing step to repair theirinputs and a post-processing step to refine their outputs. The code isavailable online: \url{https://github.com/vita-epfl/DePOSit}.

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