We present a method for transferring the style from a set of images to a 3D object. The texture appearance of an asset is optimized with a differentiable renderer in a pipeline based on losses using pretrained deep neural networks. More specifically, we utilize a nearest-neighbor feature matching loss with CLIP-ResNet50 to extract the style from images. We show that a CLIP- based style loss provides a different appearance over a VGG-based loss by focusing more on texture over geometric shapes. Additionally, we extend the loss to support multiple images and enable loss-based control over the color palette combined with automatic color palette extraction from style images.
@article{arxiv.2208.04370,
title = {CLIP-based Neural Neighbor Style Transfer for 3D Assets},
author = {Shailesh Mishra and Jonathan Granskog},
journal= {arXiv preprint arXiv:2208.04370},
year = {2022}
}