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cs.CV2026
UniCSG: Unified High-Fidelity Content-Constrained Style-Driven Generation via Staged Semantic and Frequency Disentanglement
Jingwei Yang, Ruoxi Wu, Wei Shen +4
Style transfer must match a target style while preserving content semantics. DiT-based diffusion models often suffer from content-style entanglement, leading to reference-content l…
cs.CV2026
Towards In-Context Tone Style Transfer with A Large-Scale Triplet Dataset
Yuhai Deng, Huimin She, Wei Shen +4
Tone style transfer for photo retouching aims to adapt the stylistic tone of the reference image to a given content image. However, the lack of high-quality large-scale triplet dat…