Synchronize Dual Hands for Physics-Based Dexterous Guitar Playing
Pei Xu, Ruocheng Wang
2024-09-26
Summary
This paper presents a new method for creating realistic and coordinated guitar-playing motions using simulated hands. It focuses on synchronizing the movements of both hands to mimic how a human guitarist plays.
What's the problem?
When trying to simulate guitar playing with virtual hands, it's challenging to coordinate the movements of both hands accurately. Traditional methods often treat both hands as one unit, which can make it hard to achieve the precise timing and fluidity needed for realistic playing. This can lead to unnatural motions and inaccuracies in how the guitar is played.
What's the solution?
The researchers developed a system called Synchronize Dual Hands that treats each hand as an individual agent. They first train separate policies for the left and right hands, focusing on their specific tasks. Then, they synchronize these policies using a technique that manipulates their shared information in a way that allows them to work together effectively. This method improves the efficiency of training and helps create more realistic guitar-playing motions based on unstructured reference data from actual guitar practice.
Why it matters?
This research is important because it advances the field of robotics and animation by improving how virtual characters can play musical instruments. By creating more lifelike and coordinated hand movements, this technology could enhance video games, animation, and virtual reality experiences, making them more engaging and realistic for users.
Abstract
We present a novel approach to synthesize dexterous motions for physically simulated hands in tasks that require coordination between the control of two hands with high temporal precision. Instead of directly learning a joint policy to control two hands, our approach performs bimanual control through cooperative learning where each hand is treated as an individual agent. The individual policies for each hand are first trained separately, and then synchronized through latent space manipulation in a centralized environment to serve as a joint policy for two-hand control. By doing so, we avoid directly performing policy learning in the joint state-action space of two hands with higher dimensions, greatly improving the overall training efficiency. We demonstrate the effectiveness of our proposed approach in the challenging guitar-playing task. The virtual guitarist trained by our approach can synthesize motions from unstructured reference data of general guitar-playing practice motions, and accurately play diverse rhythms with complex chord pressing and string picking patterns based on the input guitar tabs that do not exist in the references. Along with this paper, we provide the motion capture data that we collected as the reference for policy training. Code is available at: https://pei-xu.github.io/guitar.