Title
Interactive 3D Modeling with a Generative Adversarial Network
Abstract
We propose the idea of using a generative adversarial network (GAN) to assist users in designing real-world shapes with a simple interface. Users edit a voxel grid with a Minecraft-like interface. Yet they can execute a SNAP command at any time, which transforms their rough model into a desired shape that is both similar and realistic. They can edit and snap until they are satisfied with the result. The advantage of this approach is to assist novice users to create 3D models characteristic of the training data by only specifying rough edits. Our key contribution is to create a suitable projection operator around a 3D-GAN that maps an arbitrary 3D voxel input to a latent vector in the shape manifold of the generator that is both similar in shape to the input but also realistic. Experiments show our method is promising for computer-assisted interactive modeling.
Year
DOI
Venue
2017
10.1109/3DV.2017.00024
2017 International Conference on 3D Vision (3DV)
Keywords
DocType
Volume
Computer-Graphics,Interactive-Modeling,Voxel,GAN
Conference
abs/1706.05170
ISBN
Citations 
PageRank 
978-1-5386-2611-5
7
0.48
References 
Authors
28
3
Name
Order
Citations
PageRank
Jerry Liu181.52
Fisher Yu2128050.27
Thomas A. Funkhouser37616475.01