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
Full article192 words · extracted from huggingface.co · click to collapse
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. We introduce SynthGait-19k, a physically grounded synthetic video dataset containing 19,272 walking videos derived from 6,427 MoCap sequences across 437 subjects, with paired SMPL motion and annotations for six gait parameters. To construct the dataset, we develop Gait2Vid, which unifies heterogeneous MoCap recordings through SMPL and synthesizes diverse RGB walking videos under controllable viewpoints and scene appearances. We assess the generated videos for consistency with their conditioning gait kinematics and validate extracted gait events against force-platform measurements. Using SynthGait-19K, we benchmark direct RGB, pose-based, biomechanical, and human-mesh-recovery approaches and analyze viewpoint, training-data scale, and synthetic-to-real domain shift. We also introduce GaitXFormer as a direct RGB reference model for estimating gait parameters. Synthetic supervision transfers effectively to real videos across both GaitXFormer and a pose-based architecture, demonstrating utility across different representations. We further find that spatial gait parameters are more sensitive to visual domain shift and that improved HMR reconstruction alone does not necessarily translate to improved downstream gait estimation.
Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/papers/2609.08108