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DTSTAMP:20250110T023313Z
LOCATION:Hall B7 (1)\, B Block\, Level 7
DTSTART;TZID=Asia/Tokyo:20241206T145600
DTEND;TZID=Asia/Tokyo:20241206T150800
UID:siggraphasia_SIGGRAPH Asia 2024_sess150_papers_1155@linklings.com
SUMMARY:Synchronize Dual Hands for Physics-Based Dexterous Guitar Playing
DESCRIPTION:Technical Papers\n\nPei Xu and Ruocheng Wang (Stanford Univers
 ity)\n\nWe present a novel approach to synthesize dexterous motions for ph
 ysically simulated hands in tasks that require coordination between the co
 ntrol of two hands with high temporal precision. Instead of directly learn
 ing a joint policy to control two hands, our approach performs bimanual co
 ntrol through cooperative learning where each hand is treated as an indivi
 dual agent. The individual policies for each hand are first trained separa
 tely, and then synchronized through latent space manipulation in a central
 ized environment to serve as a joint policy for two-hand control. By doing
  so, we avoid directly performing policy learning in the joint state-actio
 n space of two hands with higher dimensions, greatly improving the overall
  training efficiency. We demonstrate the effectiveness of our proposed app
 roach in the challenging guitar-playing task. The virtual guitarist traine
 d by our approach can synthesize motions from unstructured reference data 
 of general guitar-playing practice motions, and accurately play diverse rh
 ythms with complex chord pressing and string picking patterns based on the
  input guitar tabs that do not exist in the references. Along with this pa
 per, we provide the motion capture data that we collected as the reference
  for policy training.\n\nRegistration Category: Full Access, Full Access S
 upporter\n\nLanguage Format: English Language\n\nSession Chair: Li-Yi Wei 
 (Adobe Research)
URL:https://asia.siggraph.org/2024/program/?id=papers_1155&sess=sess150
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