SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation
Researchers release SynthGait-19K, a synthetic video dataset with 19,272 walking videos for training gait parameter estimation models.
SynthGait-19K is a physically grounded synthetic video dataset built from 6,427 MoCap sequences of 437 subjects, yielding 19,272 walking videos with SMPL motion and annotations for six gait parameters. The authors introduce Gait2Vid, a pipeline that unifies heterogeneous MoCap recordings and synthesizes RGB videos under controllable viewpoints, validating gait events against force-platform measurements. Using the dataset they benchmark direct RGB, pose-based, biomechanical, and human-mesh-recovery approaches, and introduce GaitXFormer as a direct RGB reference model. Findings show synthetic supervision transfers to real video, while spatial gait parameters are more sensitive to visual domain shift.
- 19,272 synthetic walking videos derived from 6,427 MoCap sequences across 437 subjects
- Gait2Vid pipeline synthesizes RGB videos with controllable viewpoints and scene appearances
- Six gait parameters annotated; events validated against force-platform measurements
- GaitXFormer introduced as direct RGB reference model for gait estimation
- Spatial gait parameters found most sensitive to synthetic-to-real domain shift
- Products
- SynthGait-19KGait2VidGaitXFormer
From the original article
1 min read at huggingface.co
Accurate estimation of clinically meaningful gait parameters from monocular video is important for scalable mobility assessment, yet progress is limited by the small scale, restricted viewpoints, and limited visual diversity of existing datasets.
By Soroush Mehraban, Xin Lei Lin, Vida Adeli · huggingface.co
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