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DTSTAMP:20240214T070247Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T103000
DTEND;TZID=Australia/Melbourne:20231214T104000
UID:siggraphasia_SIGGRAPH Asia 2023_sess129_papers_586@linklings.com
SUMMARY:ShaDDR: Interactive Example-Based Geometry and Texture Generation 
 via 3D Shape Detailization and Differentiable Rendering
DESCRIPTION:Technical Papers\n\nQimin Chen, Zhiqin Chen, Hang Zhou, and Ha
 o Zhang (Simon Fraser University)\n\nWe present ShaDDR, an example-based d
 eep generative neural network which produces a high-resolution textured 3D
  shape through geometry detailization and conditional texture generation a
 pplied to an input coarse voxel shape. Trained on a small set of detailed 
 and textured exemplar shapes, our method learns to detailize the geometry 
 via multi-resolution voxel upsampling and generate textures on voxel surfa
 ces via differentiable rendering against exemplar texture images from a fe
 w views. The generation is real-time, taking less than 1 second to produce
  a 3D model with voxel resolutions up to 512^3. The generated shape preser
 ves the overall structure of the input coarse voxel model, while the style
  of the generated geometric details and textures can be manipulated throug
 h learned latent codes. In the experiments, we show that our method can ge
 nerate higher-resolution shapes with plausible and improved geometric deta
 ils and clean textures compared to prior works. Furthermore, we showcase t
 he ability of our method to learn geometric details and textures from shap
 es reconstructed from real-world photos. In addition, we have developed an
  interactive modeling application to demonstrate the generalizability of o
 ur method to various user inputs and the controllability it offers, allowi
 ng users to interactively sculpt a coarse voxel shape to define the overal
 l structure of the detailized 3D shape.\n\nRegistration Category: Full Acc
 ess\n\nSession Chair: Marc Stamminger (Friedrich-Alexander-Universität Erl
 angen-Nürnberg)
URL:https://asia.siggraph.org/2023/full-program?id=papers_586&sess=sess129
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