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.
Specifically, instead of directly modeling joint distribution over the raw
motion sequences, MCLD performs a conditional diffusion process within the
latent embedding space, characterizing the cross-modal mapping from the past
body movement and current scene context condition embeddings to the future
human motion embedding. Extensive experiments on large-scale human motion
prediction datasets demonstrate that our MCLD achieves significant improvements
over the state-of-the-art methods on both realistic and diverse predictions.

通过使用多条件潜在扩散网络，该研究提出了一种重塑人体运动预测任务的方法，该任务基于给定的历史三维身体运动和当前三维场景上下文，以实现人体运动的预测。实验结果表明，该方法在真实和多样化的预测中显著优于现有技术。