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High­level Shape Representations for Content Creation

We propose to investigate high-level representations to facilitate the creation of content, especially 3D shapes. Such methods include representing a shape as a parameterized graph of parts that can be used for shape synthesis, and can incorporate considerations on the functionality of shapes for generating plausible objects.


Funded by NSERC.

Participants
Diego Gonzalez
Oliver van Kaick


Publications

Year Title Authors Download
2021 Semantics-Guided Latent Space Exploration for Shape Generation
Tansin Jahan
Yanran Guan
Oliver van Kaick
[PDF]
2020 Generalized Autoencoder for Volumetric Shape Generation
Yanran Guan
Tansin Jahan
Oliver van Kaick
[PDF]
2018 Predictive and Generative Neural Networks for Object Functionality
Ruizhen Hu
Zihao Yan
Jingwen Zhang
Oliver van Kaick
Ariel Shamir
Hao Zhang
Hui Huang
[PDF]
2018 Functionality Representations and Applications for Shape Analysis
Ruizhen Hu
Manolis Savva
Oliver van Kaick
[PDF]
2018 3D Synthesis of Man-made Objects based on Fine-grained Parts
Diego Gonzalez
Oliver van Kaick
[PDF]
2017 Learning to Predict Part Mobility from a Single Static Snapshot
Ruizhen Hu
Wenchao Li
Oliver van Kaick
Ariel Shamir
Hao Zhang
Hui Huang
[PDF]
2017 Co-Locating Style-Defining Elements on 3D Shapes
Ruizhen Hu
Wenchao Li
Oliver van Kaick
Hui Huang
Melinos Averkiou
Daniel Cohen-Or
Hao Zhang
[PDF]
2016 Structure-oriented Networks of Shape Collections
Noa Fish
Oliver van Kaick
Amit Bermano
Daniel Cohen-Or
[PDF]
2016 Learning How Objects Function via Co-Analysis of Interactions
Ruizhen Hu
Oliver van Kaick
Bojian Wu
Hui Huang
Ariel Shamir
Hao Zhang
[PDF]
2015 SHED: Shape Edit Distance for Fine-grained Shape Similarity
Yanir Kleiman
Oliver van Kaick
Olga Sorkine-Hornung
Daniel Cohen-Or
[PDF]


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