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BEGIN:VEVENT
DTSTAMP:20260114T163633Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_486@linklings.com
SUMMARY:Neural Motion Graph
DESCRIPTION:Hongyu Tao, Shuaiying Hou, Changqing Zou, Hujun Bao, and Weiwe
 i Xu (Zhejiang University)\n\nDeep learning techniques have been employed 
 to design a controllable human motion synthesizer. Despite their potential
 , however, designing a neural network-based motion synthesis that enables 
 flexible user interaction, fine-grained controllability, and the support o
 f new types of motions at reduced time and space consumption costs remains
  a challenge. In this paper, we propose a novel approach, a neural motion 
 graph, that addresses the challenge by enabling scalability to new motions
  while using compact neural networks. Our approach represents each type of
  motion with a separate neural node to reduce the cost of adding new motio
 n types. In addition, designing a separate neural node for each motion typ
 e enables task-specific control strategies and has greater potential to ac
 hieve a high-quality synthesis of complex motions, such as the Mongolian d
 ance. Furthermore, a single transition network, which acts as neural edges
 , is used to model the transition between two motion nodes. The transition
  network is designed with a lightweight control module to achieve a fine-g
 rained response to user control signals. Overall, the design choice makes 
 the neural motion graph highly controllable and scalable. In addition to b
 eing fully flexible to user interaction through high-level and fine-graine
 d user-control signals, our experimental and subjective evaluation results
  demonstrate that our proposed approach, neural motion graph, outperforms 
 state-of-the-art human motion synthesis methods in terms of the quality of
  controlled motion generation.\n\nRegistration Category: Full Access, Enha
 nced Access, Trade Exhibitor, Experience Hall Exhibitor\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_486&sess=sess209
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