arXiv:2203.13215
공식 코드 · Papers with Code
Neural Neighbor Style Transfer
원문 초록
We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer. Our approach is based on explicitly replacing neural features extracted from the content input (to be stylized) with those from a style exemplar, then synthesizing the final output based on these rearranged features. While the spirit of our approach is similar to prior work, we show that our design decisions dramatically improve the final visual quality.
함께 살펴볼 논문
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SA-LUT: Spatial Adaptive 4D Look-Up Table for Photorealistic Style Transfer
Implicit Inversion turns CLIP into a Decoder
Pseudo Multi-Source Domain Generalization: Bridging the Gap Between Single and Multi-Source Domain Generalization
CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting
Graph Style Transfer for Counterfactual Explainability
Style Transfer with Diffusion Models for Synthetic-to-Real Domain Adaptation
토론
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