CLIP-based Neural Neighbor Style Transfer for 3D Assets

Abstract

We present a method for transferring the style from a set of images to the texture of a 3D object. The texture of an asset is optimized with a differentiable renderer and losses using pretrained deep neural networks. More specifically, we utilize a nearest-neighbor feature matching (NNFM) loss with CLIP-ResNet50 that we extend to support multiple style images. We improve color accuracy and artistic control with an extra loss on user-provided or automatically extracted color palettes. Finally, we show that a CLIP-based NNFM loss provides a different appearance over a VGG-based one by focusing more on textural details over geometric shapes. However, we note that user preference is still subjective.