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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_491@linklings.com
SUMMARY:GeoLatent: A Geometric Approach to Latent Space Design for Deforma
 ble Shape Generators
DESCRIPTION:Haitao Yang, Bo Sun, Liyan Chen, Amy Pavel, and Qixing Huang (
 University of Texas at Austin)\n\nWe study how to optimize the latent spac
 e of neural shape generators that map latent codes to 3D deformable shapes
 . The key focus is to look at a deformable shape generator from a differen
 tial geometry perspective. We define a Riemannian metric based on as-rigid
 -as-possible and as-conformal-as-possible deformation energies. Under this
  metric, we study two desired properties of the latent space: 1) straight-
 line interpolations in latent codes follow geodesic curves; 2) latent code
 s disentanglement pose and shape variations at different scales. Strictly 
 enforcing the geometric interpolation property, however, only applies if t
 he metric matrix is a constant. We show how to achieve this property appro
 ximately by enforcing that geodesic extrapolations are axis-aligned, i.e.,
  extrapolations along coordinate axis follow geodesic curves. In addition,
  we introduce a novel approach that decouples pose and shape variations vi
 a generalized eigendecomposition. We also study efficient regularization t
 erms for learning deformable shape generators, e.g., that promote smooth i
 nterpolations. Experimental results on benchmark datasets show that our ap
 proach leads to interpretable latent codes, improves the generalizability 
 of synthetic shapes, and enhances performance in geodesic interpolation, g
 eodesic shooting, and parallel translation applications.\n\nRegistration C
 ategory: Full Access, Enhanced Access, Trade Exhibitor, Experience Hall Ex
 hibitor\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_491&sess=sess209
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