Unified Style Transfer
원문 초록
Currently, it is hard to compare and evaluate different style transfer algorithms due to chaotic definitions of style and the absence of agreed objective validation methods in the study of style transfer. In this paper, a novel approach, the Unified Style Transfer (UST) model, is proposed. With the introduction of a generative model for internal style representation, UST can transfer images in two approaches, i.e., Domain-based and Image-based, simultaneously. At the same time, a new philosophy based on the human sense of art and style distributions for evaluating the transfer model is presented and demonstrated, called Statistical Style Analysis. It provides a new path to validate style transfer models' feasibility by validating the general consistency between internal style representation and art facts. Besides, the translation-invariance of AdaIN features is also discussed.
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Image-to-Image Translation with Diffusion Transformers and CLIP-Based Image Conditioning
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
Streaming 4D Visual Geometry Transformer
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
토론
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