UNIST: Unpaired Neural Implicit Shape Translation Network
This addresses shape translation for computer graphics and vision applications, but it appears incremental as it builds on existing implicit field methods.
The authors tackled the problem of unpaired shape-to-shape translation in 2D and 3D domains by introducing UNIST, a neural implicit model that enables both style-preserving content alteration and content-preserving style transfer, demonstrating generality and quality in results compared to baselines.
We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains. Our model is built on autoencoding implicit fields, rather than point clouds which represents the state of the art. Furthermore, our translation network is trained to perform the task over a latent grid representation which combines the merits of both latent-space processing and position awareness, to not only enable drastic shape transforms but also well preserve spatial features and fine local details for natural shape translations. With the same network architecture and only dictated by the input domain pairs, our model can learn both style-preserving content alteration and content-preserving style transfer. We demonstrate the generality and quality of the translation results, and compare them to well-known baselines. Code is available at https://qiminchen.github.io/unist/.