Audio Matters Too! Enhancing Markerless Motion Capture with Audio Signals for String Performance Capture

TL;DR

This paper introduces a multi-modal, markerless motion capture framework that leverages audio signals to improve the accuracy of capturing subtle string instrument performances, validated on a novel large-scale dataset.

Contribution

It presents the first multi-view, multi-modal dataset for string instrument performance and proposes an audio-guided motion capture method that enhances visual results without external markers.

Findings

Outperforms state-of-the-art vision-based algorithms

Effectively captures subtle hand-string contact movements

Demonstrates the benefit of integrating audio cues into motion capture

Abstract

In this paper, we touch on the problem of markerless multi-modal human motion capture especially for string performance capture which involves inherently subtle hand-string contacts and intricate movements. To fulfill this goal, we first collect a dataset, named String Performance Dataset (SPD), featuring cello and violin performances. The dataset includes videos captured from up to 23 different views, audio signals, and detailed 3D motion annotations of the body, hands, instrument, and bow. Moreover, to acquire the detailed motion annotations, we propose an audio-guided multi-modal motion capture framework that explicitly incorporates hand-string contacts detected from the audio signals for solving detailed hand poses. This framework serves as a baseline for string performance capture in a completely markerless manner without imposing any external devices on performers, eliminating the potential of introducing distortion in such delicate movements. We argue that the movements of performers, particularly the sound-producing gestures, contain subtle information often elusive to visual methods but can be inferred and retrieved from audio cues. Consequently, we refine the vision-based motion capture results through our innovative audio-guided approach, simultaneously clarifying the contact relationship between the performer and the instrument, as deduced from the audio. We validate the proposed framework and conduct ablation studies to demonstrate its efficacy. Our results outperform current state-of-the-art vision-based algorithms, underscoring the feasibility of augmenting visual motion capture with audio modality. To the best of our knowledge, SPD is the first dataset for musical instrument performance, covering fine-grained hand motion details in a multi-modal, large-scale collection.