Publication
SceMoS: Scene-Aware 3D Human Motion Synthesis by Planning with Geometry-Grounded Tokens
Anindita Ghosh; Vladislav Golyanik; Taku Komura; Philipp Slusallek; Christian Theobalt; Rishabh Dabral
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. International Conference on Computer Vision and Pattern Recognition (CVPR-2026), June 3-7, Denver, Colorado, CO, USA, Computer Vision Foundation (CVF), 2026.
Abstract
Synthesizing text-driven 3D human motion within realistic scenes requires learning both semantic intent (“walk to the couch”) and physical feasibility (e.g., avoiding collisions). Current methods use generative frameworks that simultaneously learn high-level planning and low-level contact reasoning, and rely on computationally expensive 3D scene data such as point clouds or voxel occupancy grids. We propose SceMoS, a scene-aware motion synthesis framework that shows that structured 2D scene representations can serve as a powerful alternative to full 3D supervision in physically grounded motion synthesis. SceMoS disentangles global planning from local execution using lightweight 2D cues and relying on (1) a text-conditioned autoregressive global motion planner that operates on a bird’s-eye-view (BEV) image of the scene taken from an elevated corner, encoded with DINOv2 features, as the scene representation, and (2) a geometry-grounded motion tokenizer trained via a conditional VQ-VAE, that uses 2D local scene heightmap, thus embedding surface physics directly into a discrete vocabulary. This 2D factorization reaches an efficiency-fidelity trade-off: BEV semantics capture spatial layout and affordance for global reasoning, while local heightmaps enforce fine-grained physical adherence without full 3D volumetric reasoning. SceMoS achieves state-of-the-art motion realism and contact accuracy on the TRUMANS benchmark, reducing the number of trainable parameters for scene encoding by over 50%, showing that 2D scene cues can effectively ground 3D human-scene interaction.
Projects
- MOMENTUM - Robust Learning with Hybrid AI for Trustworthy Interaction of Humans and Machines in Complex Environments
- TwinMaP - Digital twin of a heterogeneous machine park for the complete machining of components
- Intel/Saar LCAAM - Latency Compensation for Avatar Animations in the Metaverse