2017/12/09 by Utkarsh Mall, G. Roshan Lal, Mall, Utkarsh +6 · 1 citation
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Graphics (cs.GR) #Human Motion and Animation #Human Pose and Action Recognition #Video Analysis and Summarization #cs.CV #cs.GR
paper · pdf · doi:10.48550/arxiv.1712.03380
arxiv created 2017/12/09 · openalex publication_date 2017/12/09 · arxiv updated 2017/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a deep, bidirectional, recurrent framework for cleaning noisy and incomplete motion capture data. It exploits temporal coherence and joint correlations to infer adaptive filters for each joint in each frame. A single model can be trained to denoise a heterogeneous mix of action types, under substantial amounts of noise. A signal that has both noise and gaps is preprocessed with a second bidirectional network that synthesizes missing frames from surrounding context. The approach handles a wide variety of noise types and long gaps, does not rely on knowledge of the noise distribution, and operates in a streaming setting. We validate our approach through extensive evaluations on noise both in joint angles and in joint positions, and show that it improves upon various alternatives.