视频风格化终于能只改局部，不碰背景
现在的视频风格化都是一锅端：整段画面全变成油画或水墨，你想只把画面里的一个人变成卡通、背景保持原样，做不到。这篇把「改哪里」和「改成什么样」拆成两路分别控制：先用一个带风格信息的掩码圈定区域，再把区域的空间位置和视频的时序信息分开喂给模型，互不干扰。训练数据是自动生成的——先全局风格化、再抠掉目标物体、合成回去，凑出10万对「局部改前/改后」样本。效果上，它还能在同一个视频里给不同区域上不同风格，尽管训练时只见过单区域单风格。做短视频特效、广告后期、影视局部修图的人，这是能直接拿来用的方向。
📄 原文摘要(英文)
Video stylization typically transforms entire scenes, while local artistic editing requires precise region--style association, spatial control, and temporal consistency. We introduce LociArt, a unified paradigm for free-style local video stylization that explicitly specifies where to stylize and what style to apply while preserving non-target content. At its core, GRNStyle builds on Generative Refinement Networks (GRN) and introduces a polar style-aware mask that embeds style identities as equal-norm directions within selected regions. Complementary representation--geometry conditioning combines encoded mask features with raw-mask spatial support, integrating spatiotemporal context with geometric control. To provide paired supervision, we develop an automated pipeline combining global stylization, video object removal, matting, and compositing, and construct the LociArt-100K Dataset with 100K local stylization pairs. We further establish the LociArt Benchmark, comprising 80 source videos and 84 target masks covering 168 foreground/background stylization cases, with a Style Prototype Similarity (SPS) evaluation scheme for regional style quality and complementary measures of non-target preservation and temporal consistency. Experiments show that GRNStyle outperforms existing methods in regional style quality and spatial control while maintaining strong temporal consistency. Qualitative examples further illustrate joint stylization of multiple regions with the same or different learned styles, despite training only on single-region, single-style pairs. Together, these components provide a unified foundation for learning and evaluating controllable local video stylization. Project page: https://higherhu.github.io/lociart/.