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PRODID:Linklings LLC
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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035348Z
LOCATION:Room 325-AB\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221208T110000
DTEND;TZID=Asia/Seoul:20221208T123000
UID:siggraphasia_SIGGRAPH Asia 2022_sess168@linklings.com
SUMMARY:Shape Generation
DESCRIPTION:Technical Papers\n\nThe presentations will be followed by a 30
 -min Interactive Discussion Session at Room 325-CD.\n\nThe Technical Paper
 s program is the premier international forum for disseminating new scholar
 ly work in computer graphics and interactive techniques. Technical Papers 
 are published as a special issue of ACM Transactions on Graphics. In addit
 ion to papers selected by the SIGGRAPH Asia 2022 Technical Papers Jury, th
 e conference presents papers that have been published in ACM Transactions 
 on Graphics during the past year. Accepted papers adhere to the highest sc
 ientific standards.\n\nLayoutEnhancer: Generating Good Indoor Layouts from
  Imperfect Data\n\nLeimer, Guerrero, Weiss, Musialski\n\nWe address the pr
 oblem of indoor layout synthesis, which is a topic of continuing research 
 interest in computer graphics. The newest works made significant progress 
 using data-driven generative methods; however, these approaches rely on su
 itable datasets. In practice, some desirable layout propertie...\n\n------
 ---------------\nCLIP-Mesh: Generating textured meshes from text using pre
 trained image-text models\n\nMohammad Khalid, Xie, Belilovsky, Popa\n\nWe 
 present a technique for zero-shot generation of a 3D model using only a ta
 rget text prompt. Without any 3D supervision our method deforms the contro
 l shape of a limit subdivided surface along with its texture map and norma
 l map to obtain a 3D asset that corresponds to the input text prompt and c
 a...\n\n---------------------\nLearning to Generate 3D Shapes from a Singl
 e Example\n\nWu, Zheng\n\nExisting generative models for 3D shapes are typ
 ically trained on a large 3D dataset, often of a specific object category.
   In this paper, we investigate the deep generative model that learns from
  only a single reference 3D shape. Specifically, we present a multi-scale 
 GAN-based model designed to ca...\n\n---------------------\nNeural Wavelet
 -domain Diffusion for 3D Shape Generation\n\nHui, Li, Hu, Fu\n\nThis paper
  presents a new approach for 3D shape generation, enabling a direct genera
 tive modeling on a continuous implicit representation in wavelet frequency
  domain. Specifically, we propose a compact wavelet representation with a 
 pair of coarse and detail coefficient volumes to implicitly represen...\n\
 n---------------------\nScene Synthesis from Human Motion\n\nYe, Wang, Li,
  Park, Liu...\n\nLarge-scale capture of human motion with diverse, complex
  scenes, while immensely useful, is often considered prohibitively costly.
  Meanwhile, human motion alone contains rich information about the scene t
 hey reside in and interact with. For example, a sitting human suggests the
  existence of a chair...\n\n---------------------\nExact 3D Path Generatio
 n via 3D Cam-Linkage Mechanisms\n\nCheng, Song, Lu, Chew, Liu\n\nExact 3D 
 path generation is a fundamental problem of designing a mechanism to make 
 a point exactly move along a prescribed 3D path, driven by a single actuat
 or. Existing mechanisms are insufficient to address this problem. Planar l
 inkages and their combinations with gears and/or plate cams can only ...\n
 \n\nRegistration Category: FULL ACCESS, ON-DEMAND ACCESS\n\nLanguage: ENGL
 ISH\n\nFormat: IN-PERSON, ON-DEMAND
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