Regional Style and Color Transfer
<|reference_start|>Regional Style and Color Transfer: This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire image, leading to stylistic inconsistencies or foreground object twisted when applied to image with foreground elements such as person figures. To address this limitation, we propose a new approach that leverages a segmentation network to precisely isolate foreground objects within the input image. Subsequently, style transfer is applied exclusively to the background region. The isolated foreground objects are then carefully reintegrated into the style-transferred background. To enhance the visual coherence between foreground and background, a color transfer step is employed on the foreground elements prior to their rein-corporation. Finally, we utilize feathering techniques to achieve a seamless amalgamation of foreground and background, resulting in a visually unified and aesthetically pleasing final composition. Extensive evaluations demonstrate that our proposed approach yields significantly more natural stylistic transformations compared to conventional methods.<|reference_end|>
@article{ding2024regional,
    title={Regional Style and Color Transfer},
    author={Zhicheng Ding, Panfeng Li, Qikai Yang, Siyang Li, Qingtian Gong},
    journal={Proceedings of the 2024 5th International Conference on Computer
  Vision, Image and Deep Learning (CVIDL), 2024, pp. 593-597},
    year={2024},
    doi={10.1109/CVIDL62147.2024.10604182},
    archivePrefix={arXiv},
    eprint={2404.13880},
    primaryClass={cs.CV cs.AI cs.LG}
}