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UniCSG: Unified High-Fidelity Content-Constrained Style-Driven Generation via Staged Semantic and Frequency Disentanglement

2026-04-20 05:59:20
Jingwei Yang, Ruoxi Wu, Wei Shen, Meng Li, Yulong Liu, Huimin She, Lunxi Yuan
arXiv_CV
       
Abstract

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 leakage and unstable generation. We present UniCSG, a unified framework for content-constrained, style-driven generation in both text-guided and reference-guided settings. UniCSG employs staged training: (i) a latent-space semantic disentanglement stage that combines low-frequency preprocessing with conditioning corruption to encourage content-style separation, and (ii) a latent-space frequency-aware detail reconstruction stage that refines details via multi-scale frequency supervision. We further incorporate pixel-space reward learning to align latent objectives with perceptual quality after decoding. Experiments demonstrate improved content faithfulness, style alignment, and robustness in both settings.

Abstract (translated)

URL

https://arxiv.org/abs/2604.17850

PDF

https://arxiv.org/pdf/2604.17850.pdf

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