Semantix: An Energy Guided Sampler for Semantic Style Transfer (2503.22344v1)

Abstract: Recent advances in style and appearance transfer are impressive, but most methods isolate global style and local appearance transfer, neglecting semantic correspondence. Additionally, image and video tasks are typically handled in isolation, with little focus on integrating them for video transfer. To address these limitations, we introduce a novel task, Semantic Style Transfer, which involves transferring style and appearance features from a reference image to a target visual content based on semantic correspondence. We subsequently propose a training-free method, Semantix an energy-guided sampler designed for Semantic Style Transfer that simultaneously guides both style and appearance transfer based on semantic understanding capacity of pre-trained diffusion models. Additionally, as a sampler, Semantix be seamlessly applied to both image and video models, enabling semantic style transfer to be generic across various visual media. Specifically, once inverting both reference and context images or videos to noise space by SDEs, Semantix utilizes a meticulously crafted energy function to guide the sampling process, including three key components: Style Feature Guidance, Spatial Feature Guidance and Semantic Distance as a regularisation term. Experimental results demonstrate that Semantix not only effectively accomplishes the task of semantic style transfer across images and videos, but also surpasses existing state-of-the-art solutions in both fields. The project website is available at https://huiang-he.github.io/semantix/