Depth-Guided Metric-Aware Temporal Consistency for Monocular Video Human Mesh Recovery
This work addresses metric consistency and temporal stability issues in monocular video human mesh recovery, which is important for applications like animation and AR/VR, though it appears incremental as it builds on existing depth-guided approaches.
The paper tackles the problem of maintaining metric consistency and temporal stability in monocular video human mesh recovery by proposing a depth-guided framework with three synergistic components. The method achieves superior results on three challenging benchmarks with significant improvements in robustness against heavy occlusion and spatial accuracy.
Monocular video human mesh recovery faces fundamental challenges in maintaining metric consistency and temporal stability due to inherent depth ambiguities and scale uncertainties. While existing methods rely primarily on RGB features and temporal smoothing, they struggle with depth ordering, scale drift, and occlusion-induced instabilities. We propose a comprehensive depth-guided framework that achieves metric-aware temporal consistency through three synergistic components: A Depth-Guided Multi-Scale Fusion module that adaptively integrates geometric priors with RGB features via confidence-aware gating; A Depth-guided Metric-Aware Pose and Shape (D-MAPS) estimator that leverages depth-calibrated bone statistics for scale-consistent initialization; A Motion-Depth Aligned Refinement (MoDAR) module that enforces temporal coherence through cross-modal attention between motion dynamics and geometric cues. Our method achieves superior results on three challenging benchmarks, demonstrating significant improvements in robustness against heavy occlusion and spatial accuracy while maintaining computational efficiency.