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DTSTAMP:20240214T070244Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T132100
DTEND;TZID=Australia/Melbourne:20231213T133600
UID:siggraphasia_SIGGRAPH Asia 2023_sess163_papers_221@linklings.com
SUMMARY:Commonsense Knowledge-Driven Joint Reasoning Approach for Object R
 etrieval in Virtual Reality
DESCRIPTION:Technical Papers\n\nHaiyan Jiang (Beijing Institute of Technol
 ogy; National Key Laboratory of General Artificial Intelligence, Beijing I
 nstitute for General Artificial Intelligence (BIGAI)); Dondong Weng (Beiji
 ng Institute of Technology); Xiaonuo Dongye (Beijing Institute of Technolo
 gy; National Key Laboratory of General Artificial Intelligence, Beijing In
 stitute for General Artificial Intelligence (BIGAI)); Le Luo (Beijing Inst
 itute of Technology); and Zhenliang Zhang (National Key Laboratory of Gene
 ral Artificial Intelligence, Beijing Institute for General Artificial Inte
 lligence (BIGAI))\n\nOut-of-reach object retrieval is an important task in
  virtual reality (VR). Gesture-based approach, one of the most commonly us
 ed approaches, enables bare-hand, eyes-free, and direct retrieval by using
  assigned gestures. However, it is difficult to retrieve an object from pl
 enty of objects accurately by using gestures due to the one-to-one mapping
  metaphor, the limitation of finger poses, and memory burdens. Previous wo
 rk has focused on gesture design, ignoring the context. In fact, there is 
 a consensus that objects and contexts are related. This indicates the obje
 ct expected to be retrieved is related to the context including the scene 
 and the objects users interact with. Therefore, we proposed a commonsense 
 knowledge-driven joint reasoning approach for object retrieval, where the 
 human grasping gesture and the context are modeled by an And-Or graph (AOG
 ). This approach enables users to accurately retrieve objects from plenty 
 of candidate objects by using natural grasping gestures according to their
  experience of grasping physical objects. The experimental results show th
 at our proposed model improves retrieval accuracy. Finally, we propose an 
 object retrieval system based on the proposed approach and two user studie
 s demonstrate that the system enables efficient object retrieval in virtua
 l environments.\n\nRegistration Category: Full Access\n\nSession Chair: Ch
 ek Tien Tan (Singapore Institute of Technology)
URL:https://asia.siggraph.org/2023/full-program?id=papers_221&sess=sess163
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