Motion synthesis in real-world 3D scenes has recently attracted much
attention. However, the static environment assumption made by most current
methods usually cannot be satisfied especially for real-time motion synthesis
in scanned point cloud scenes, if multiple dynamic objects exist, e.g., moving
persons or vehicles. To handle this problem, we propose the first Dynamic
Environment MOtion Synthesis framework (DEMOS) to predict future motion
instantly according to the current scene, and use it to dynamically update the
latent motion for final motion synthesis. Concretely, we propose a
Spherical-BEV perception method to extract local scene features that are
specifically designed for instant scene-aware motion prediction. Then, we
design a time-variant motion blending to fuse the new predicted motions into
the latent motion, and the final motion is derived from the updated latent
motions, benefitting both from motion-prior and iterative methods. We unify the
data format of two prevailing datasets, PROX and GTA-IM, and take them for
motion synthesis evaluation in 3D scenes. We also assess the effectiveness of
the proposed method in dynamic environments from GTA-IM and Semantic3D to check
the responsiveness. The results show our method outperforms previous works
significantly and has great performance in handling dynamic environments.

该研究提出了第一个动态环境动作合成框架（DEMOS），通过预测当前场景来即时预测未来动作，并用其动态更新潜在动作，从而在处理动态环境时取得了显著的性能优势。