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DTSTART:18871231T000000
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BEGIN:VEVENT
DTSTAMP:20250110T023312Z
LOCATION:Hall B7 (1)\, B Block\, Level 7
DTSTART;TZID=Asia/Tokyo:20241204T104500
DTEND;TZID=Asia/Tokyo:20241204T115500
UID:siggraphasia_SIGGRAPH Asia 2024_sess114@linklings.com
SUMMARY:Your Wish is my Command: Generate, Edit, Rearrange
DESCRIPTION:Technical Papers\n\nEach Paper gives a 10 minute presentation.
 \n\nLLM-enhanced Scene Graph Learning for Household Rearrangement\n\nThe h
 ousehold rearrangement task involves spotting misplaced objects in a scene
  and accommodate  them with proper places. It depends both on common-sense
  knowledge on the objective side and human user preference on the subjecti
 ve side. In achieving such task, we propose to mine object functionality .
 ..\n\n\nWenhao Li, Zhiyuan Yu, Qijin She, Zhinan Yu, Yuqing Lan, and Cheny
 ang Zhu (National University of Defense Technology (NUDT)); Ruizhen Hu (Sh
 enzhen University (SZU)); and Kai Xu (National University of Defense Techn
 ology (NUDT))\n---------------------\nSGEdit: Bridging LLM with Text2Image
  Generative Model for Scene Graph-based Image Editing\n\nScene graphs offe
 r a structured, hierarchical representation of images, with nodes and edge
 s symbolizing objects and the relationships among them. It can serve as a 
 natural interface for image editing, dramatically improving precision and 
 flexibility. Leveraging this benefit, we introduce a new fram...\n\n\nZhiy
 uan Zhang (City University of Hong Kong), DongDong Chen (Microsoft GenAI),
  and Jing Liao (City University of Hong Kong)\n---------------------\nCPos
 er: An Optimization-after-Parsing Approach for Text-to-Pose Generation Usi
 ng Large Language Models.\n\nText-to-pose generation is challenging due to
  the complexity of natural language and human posture semantics. Utilizing
  large language models (LLMs) for text-to-pose generation is appealing due
  to their strong capabilities in text understanding and reasoning. However
 , as LLMs are designed for genera...\n\n\nYumeng Li, Bohong Chen, Zhong Re
 n, and Yao-Xiang Ding (Zhejiang University); Libin Liu (Peking University)
 ; and Tianjia Shao and Kun Zhou (Zhejiang University)\n-------------------
 --\nAnim-Director: A Large Multimodal Model Powered Agent for Controllable
  Animation Video Generation\n\nTraditional animation generation methods de
 pend on training generative models with human-labelled data, entailing a s
 ophisticated multi-stage pipeline that demands substantial human effort an
 d incurs high training costs. Due to limited prompting plans, these method
 s typically produce brief, informat...\n\n\nYunxin Li, Haoyuan Shi, and Ba
 otian Hu (Harbin Institute of Technology); Longyue Wang (Alibaba Group); J
 iashun Zhu and Jinyi Xu (Jilin University); Zhen Zhao (Tencent AILab); and
  Min Zhang (Harbin Institute of Technology)\n---------------------\nParSEL
 : Parameterized Shape Editing with Language\n\nThe ability to edit 3D asse
 ts from natural language presents a compelling paradigm to aid in the demo
 cratization of 3D content creation. However, while natural language is oft
 en effective at communicating general intent, it is poorly suited for spec
 ifying exact manipulation. To address this gap, we ...\n\n\nAditya Ganesha
 n, Ryan Huang, Xianghao Xu, R. Kenny Jones, and Daniel Ritchie (Brown Univ
 ersity)\n---------------------\nAutonomous Character-Scene Interaction Syn
 thesis from Text Instruction\n\nSynthesizing human motions in 3D environme
 nts, particularly those with complex activities such as locomotion, hand-r
 eaching, and human-object interaction, presents substantial demands for us
 er-defined waypoints and stage transitions. These requirements pose challe
 nges for current models, leading to ...\n\n\nNan Jiang (Peking University,
  Beijing Institute for General Artificial Intelligence); Zimo He (Peking U
 niversity); Zi Wang (Beijing University of Posts and Telecommunications); 
 Hongjie Li (Peking University); Yixin Chen and Siyuan Huang (Beijing Insti
 tute for General Artificial Intelligence); and Yixin Zhu (Peking Universit
 y)\n\nRegistration Category: Full Access, Full Access Supporter\n\nLanguag
 e Format: English Language\n\nSession Chair: Kai Wang (Amazon)
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