Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction
Cecilia Curreli, Dominik Muhle, Abhishek Saroha, Zhenzhang Ye, Riccardo Marin, Daniel Cremers
2025Year
6Top-tier citations
Abstract
P as t G T F u tu re P re d ic te d fu tu re s C lo se st D iv er se #1 D iv er se #2 D iv er se #3 Figure 1. SkeletonDiffusion generates futures that are simultaneously diverse and realistic. With a nonisotropic diffusion formulation reflecting the skeleton structure, we predict motions that are plausible and semantically coherent with the input past while being highly diverse. Here, we show the most diverse ensemble of three motions including the prediction closest to the ground truth among 50 generated futures.
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Cited by top-tier papers6
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- Gaussian-Mixture Latent Flow for Stochastic 3D Human Motion PredictionYue Ma, Frederick W. B. Li, Xiaohui LiangCVPR 2026
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