  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/.
