Unified skeleton representation learning for autonomous driving
• MotionMoCo learns generic skeleton representations that are adaptable to different downstream tasks. • MotionMoCo learns unified skeleton representations by decoupling spatial and temporal features. • A momentum contrastive framework ensures robust and transferable spatiotemporal embeddings. • Motion-Aware Frame Masking selectively removes fast-moving frames to enhance feature learning. • Achieves competitive results on action recognition, 2D-to-3D lifting, and human mesh recovery. • Enables multi-task generalization without requiring task-specific annotations or architectures.
Authors
- Mohamed Abdelfattah (ORCID: https://orcid.org/0009-0002-5732-4340)
- Alexandre Alahi (ORCID: https://orcid.org/0000-0002-5004-1498)
Institutions
- École Polytechnique Fédérale de Lausanne (CH)
Publication Details
- Journal
- Transportation Research Part C Emerging Technologies
- Published
- 2026-05-23
- DOI
- https://doi.org/10.1016/j.trc.2026.105753
- Primary Topic
- Reinforcement Learning in Robotics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung