ShapeShifter: 3D Variations Using Multiscale and Sparse Point-Voxel Diffusion
This work provides a more efficient and detailed 3D generative model for computer graphics and design applications, though it appears incremental in its hybrid approach.
The paper tackles the problem of generating detailed 3D shape variations from a single reference model, addressing limitations in geometric detail and computational efficiency of existing methods, and demonstrates that their approach captures fine details better than previous SDF-based methods.
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.