Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction

TL;DR

SkeletonDiffusion is a novel latent diffusion model for human motion prediction that incorporates human body structure, producing more realistic and artifact-free motion predictions compared to traditional isotropic methods.

Contribution

The paper introduces SkeletonDiffusion, a nonisotropic Gaussian diffusion model that embeds human kinematic bias, improving realism and consistency in 3D human motion prediction.

Findings

Outperforms isotropic diffusion models in realism and artifact reduction

Sets new benchmarks on real-world datasets

Identifies limitations of current diversity metrics

Abstract

Probabilistic human motion prediction aims to forecast multiple possible future movements from past observations. While current approaches report high diversity and realism, they often generate motions with undetected limb stretching and jitter. To address this, we introduce SkeletonDiffusion, a latent diffusion model that embeds an explicit inductive bias on the human body within its architecture and training. Our model is trained with a novel nonisotropic Gaussian diffusion formulation that aligns with the natural kinematic structure of the human skeleton. Results show that our approach outperforms conventional isotropic alternatives, consistently generating realistic predictions while avoiding artifacts such as limb distortion. Additionally, we identify a limitation in commonly used diversity metrics, which may inadvertently favor models that produce inconsistent limb lengths within the same sequence. SkeletonDiffusion sets a new benchmark on real-world datasets, outperforming various baselines across multiple evaluation metrics. Visit our project page at https://ceveloper.github.io/publications/skeletondiffusion/ .