LociArt: A Unified Paradigm for Free-Style Local Video Stylization
LociArt: A Unified Paradigm for Free-Style Local Video Stylization
LociArt achieves precise local video stylization through generating refined networks, automated dataset construction, and dedicated benchmarks, supporting regional style control and temporal consistency. GRNStyle introduces a polarity-aware style perception mask on the basis of generating refined networks, combined with representation-geometric conditioning technology. Experiments show that it outperforms existing methods in regional style quality and spatial control. The LociArt-100K dataset contains 100,000 local stylization pairs, the benchmark includes 80 source videos and 84 target masks, using a style prototype similarity evaluation scheme.
Published on Oct 27
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Abstract
LociArt enables precise local video stylization with region-style control and temporal consistency via a generative refinement network, automated dataset construction, and a dedicated benchmark.
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/.
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