Interactive 3D Modeling with a Generative Adversarial Network
Jerry Liu, Fisher Yu, Thomas Funkhouser

TL;DR
This paper introduces a novel interactive 3D modeling method using GANs to help users create realistic shapes by snapping their voxel edits onto a learned shape manifold, enhancing novice modeling.
Contribution
It presents the first application of GANs for interactive 3D modeling, developing algorithms for shape projection and integrating them into a user-friendly tool.
Findings
GAN-based projection supports realistic shape creation
The tool assists novices in designing 3D models
Experiments show promising results for GAN-assisted modeling
Abstract
This paper proposes the idea of using a generative adversarial network (GAN) to assist a novice user in designing real-world shapes with a simple interface. The user edits a voxel grid with a painting interface (like Minecraft). Yet, at any time, he/she can execute a SNAP command, which projects the current voxel grid onto a latent shape manifold with a learned projection operator and then generates a similar, but more realistic, shape using a learned generator network. Then the user can edit the resulting shape and snap again until he/she is satisfied with the result. The main advantage of this approach is that the projection and generation operators assist novice users to create 3D models characteristic of a background distribution of object shapes, but without having to specify all the details. The core new research idea is to use a GAN to support this application. 3D GANs have…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis · Advanced Vision and Imaging
