Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction

Stochastic human motion prediction aims to forecast multiple plausible future motions given a single pose sequence from the past. Most previous works focus on designing elaborate losses to improve the accuracy, while the diversity is typically characterized by randomly sampling a set of latent variables from the latent prior, which is then decoded into possible motions. This joint training of sampling and decoding, however, suffers from posterior collapse as the learned latent variables tend to be ignored by a strong decoder, leading to limited diversity. Alternatively, inspired by the diffusion process in nonequilibrium thermodynamics, we propose MotionDiff, a diffusion probabilistic model to treat the kinematics of human joints as heated particles, which will diffuse from original states to a noise distribution. This process not only offers a natural way to obtain the "whitened'' latents without any trainable parameters, but also introduces a new noise in each diffusion step, both of which facilitate more diverse motions. Human motion prediction is then regarded as the reverse diffusion process that converts the noise distribution into realistic future motions conditioned on the observed sequence. Specifically, MotionDiff consists of two parts: a spatial-temporal transformer-based diffusion network to generate diverse yet plausible motions, and a flexible refinement network to further enable geometric losses and align with the ground truth. Experimental results on two datasets demonstrate that our model yields the competitive performance in terms of both diversity and accuracy.

Authors

• Dong Wei School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
• Huaijiang Sun School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
• Bin Li
• Jianfeng Lu Wuhan University of Science and Technology Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education, China
• Weiqing Li School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
• Xiaoning Sun School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
• Shengxiang Hu Shanghai University

Keywords

• CV: Motion & Tracking
• HAI: Human-Aware Planning and Behavior Prediction
• ML: Deep Generative Models & Autoencoders