Multi-Condition Latent Diffusion Network for Scene-Aware Neural Human Motion Prediction

Understand

Inferring 3D human motion is fundamental in many applications, including understanding human activity and analyzing one's intention.

• While many fruitful efforts have been made to human motion prediction, most approaches focus on pose-driven prediction and inferring human motion in isolation from the contextual environment, thus leaving the body location movement in the scene behind.
• However, real-world human movements are goal-directed and highly influenced by the spatial layout of their surrounding scenes.
• In this paper, instead of planning future human motion in a 'dark' room, we propose a Multi-Condition Latent Diffusion network (MCLD) that reformulates the human motion prediction task as a multi-condition joint inference problem based on the given historical 3D body motion and the current 3D scene contexts.