Kavli Affiliate: Matthew Fisher
| First 5 Authors: Nissim Maruani, Wang Yifan, Matthew Fisher, Pierre Alliez, Mathieu Desbrun
| Summary:
This paper proposes ShapeShifter, a new 3D generative model that learns to
synthesize shape variations based on a single reference model. While generative
methods for 3D objects have recently attracted much attention, current
techniques often lack geometric details and/or require long training times and
large resources. Our approach remedies these issues by combining sparse voxel
grids and point, normal, and color sampling within a multiscale neural
architecture that can be trained efficiently and in parallel. We show that our
resulting variations better capture the fine details of their original input
and can handle more general types of surfaces than previous SDF-based methods.
Moreover, we offer interactive generation of 3D shape variants, allowing more
human control in the design loop if needed.
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