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We present a method for transferring the style from a set of images to a 3Dobject. The texture appearance of an asset is optimized with a differentiablerenderer in a pipeline based on losses using pretrained deep neural networks.More specifically,  we utilize a nearest-neighbor feature matching loss with CLIP-ResNet50 toextract the style from images. We show that a CLIP- based style loss provides adifferent appearance over a VGG-based loss by focusing more on texture overgeometric shapes.  Additionally, we extend the loss to support multiple images and enableloss-based control over the color palette combined with automatic color paletteextraction from style images.

CLIP-based Neural Neighbor Style Transfer for 3D Assets