- Title Information
- Title
- Learning Structure-Aware Generative Models of 3D Characters
- Abstract
- Recent advances in deep learning have enabled new generative models for 3D objects, in particular manufactured shapes (e.g. chairs, tables, airplanes, cars, ...). The most state-of-the-art methods in this space are "structure-aware": they take into account the parts of which the object is made and how those parts are assembled. However, there has been relatively little progress in generating 3D characters: people, animals, and other such 'living agents.' Generative models of characters would be useful for content creation in animation, visual effects, and games; they would also find application in 3D reconstruction applications (for example, marker-less tracking and reconstruction of animals). Character modeling presents unique challenges not shared by manufactured shapes: characters can vary widely in body structure, shape, and pose, and they typically do not decompose into assemblies of parts (since they are mostly organic). How can we design structure-aware generative models of 3D characters and learn them from data? This project seeks to answer this question. Our hypothesis is that a good representation for a character's structure is its skeleton.
- Name:
Personal
- Name Part
- Zhan, Xiao
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Daniel Ritchie
- Role
- Role Term:
Text
- advisor
- affiliation
- Brown University. Computer Science
- Name:
Corporate
- Name Part
- Brown University. Karen T. Romer Undergraduate Teaching and Research Awards
- Role
- Role Term:
Text
- research program
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01036260")
- Topic
- Neural networks (Computer science)
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01004795")
- Topic
- Machine learning
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00872687")
- Topic
- Computer vision
- Language
- Language Term:
Text (ISO639-2B)
- English
- Type of Resource
- still image
- Genre (aat)
- posters
- Origin Information
- Place
- Place Term:
Code (MARC Country Code)
- riu
- Place
- Place Term:
Text
- Providence, RI
- Publisher
- Brown University
- Date Created
(keyDate="yes", encoding="w3cdtf")
- 2021
- Physical Description
- Extent
- 1 poster
- digitalOrigin
- born digital
- Access Condition:
use and reproduction
- All rights reserved
- Access Condition:
rights statement
(href="http://rightsstatements.org/vocab/InC/1.0/")
- In Copyright
- Access Condition:
restriction on access
- All Rights Reserved
- Identifier:
DOI
- 10.26300/9tz8-v671