XmoPipe: A Pipeline for Large-Scale In-the-Wild Human Motion Dataset Construction
Large-scale human motion datasets are essential for training robust motion models for analysis, synthesis, and understanding. While marker-based motion capture provides precise data, it is costly and limited in scale and diversity. Recent advances in monocular motion capture and video-language understanding open the way to extract plausible motion from unconstrained online videos. We present a scalable pipeline for constructing in-the-wild human motion datasets. From a few keywords, the system retrieves videos, extracts 3D body and facial motion, and generates high-level textual descriptions. The pipeline is flexible, enabling targeted collection of various motions, multi-person interacti...
| Authors | Nathan Salazar, Emmanuel Dellandréa, Mathieu Lefort et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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