# A Deep Recurrent Framework for Cleaning Motion Capture Data

arXiv: https://arxiv.org/abs/1712.03380 · HTML: https://mlsanity.com/paper/1712.03380/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2017.

## Built on by (most cited first, 3 of 3)

- [SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos](https://mlsanity.com/paper/2112.13715/) (2021)
- [Graph-based Normalizing Flow for Human Motion Generation and Reconstruction](https://mlsanity.com/paper/2104.03020/) (2021)
- [StableMotion: Training Motion Cleanup Models with Unpaired Corrupted Data](https://mlsanity.com/paper/2505.03154/) (2025)

## Abstract (arXiv)

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.