PT Unknown
AU Reza Azad
   Afshin Bozorgpour
   Maryam Asadi-Aghbolaghi
   Dorit Merhof
   Sergio Escalera
TI Deep Frequency Re-Calibration U-Net for Medical Image Segmentation
BT IEEE/CVF International Conference on Computer Vision Workshops
PY 2021
BP 3274
EP 3283
AB We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality.
ER