  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.
