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CLIP-based Neural Neighbor Style Transfer for 3D Assets
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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Stable Attribution
When an A.I. model is trained to create images from text, it uses a huge dataset of images and their corresponding captions. The model is trained by showing it the captions, and having it try to recreate the images associated with each one, as closely as possible.