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arXiv · 2208.04370

CLIP-based Neural Neighbor Style Transfer for 3D Assets

Shailesh Mishra·Jonathan Granskog
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Abstract

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.

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Shailesh Mishra, Jonathan Granskog. 2022-08-08. CLIP-based Neural Neighbor Style Transfer for 3D Assets. https://arxiv.org/abs/2208.04370

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