A Deep Recurrent Framework for Cleaning Motion Capture Data

TL;DR

This paper introduces a deep recurrent neural network framework that effectively cleans and reconstructs noisy, incomplete motion capture data by leveraging temporal and joint correlations, suitable for real-time applications.

Contribution

The authors propose a novel bidirectional recurrent model capable of denoising and filling gaps in motion capture data without prior noise distribution knowledge.

Findings

Outperforms existing methods in noise reduction accuracy

Effectively handles long gaps and diverse noise types

Operates efficiently in streaming scenarios

Abstract

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.