Explorable Mesh Deformation Subspaces from Unstructured 3D Generative Models-Reference-Cited by-同舟云学术

Explorable Mesh Deformation Subspaces from Unstructured 3D Generative Models

Published:2023-12-10 Issue: Volume: Page:
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Container-title:SIGGRAPH Asia 2023 Conference Papers
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Author:

Maesumi Arman¹^ORCID,Guerrero Paul²^ORCID,Aigerman Noam³^ORCID,Kim Vladimir⁴^ORCID,Fisher Matthew⁴^ORCID,Chaudhuri Siddhartha⁵^ORCID,Ritchie Daniel¹^ORCID

Affiliation:

1. Brown University, United States of America

2. Adobe Inc., United Kingdom

3. Adobe Inc., United States of America and University of Montreal, Canada

4. Adobe Inc., United States of America

5. Adobe Inc., India and Indian Institute of Technology (IIT), Bombay, India

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3610548.3618192

Reference57 articles.

1. Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas J. Guibas. 2017. Learning Representations and Generative Models for 3D Point Clouds. In International Conference on Machine Learning.

2. Neural jacobian fields

3. Scattered data interpolation for computer graphics

4. Melinos Averkiou, Vladimir G. Kim, Youyi Zheng, and Niloy J. Mitra. 2014. ShapeSynth: Parameterizing Model Collections for Coupled Shape Exploration and Synthesis. Computer Graphics Forum (Proc. of Eurographics) 33, 2 (2014).

5. On Linear Variational Surface Deformation Methods

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