Video stylization method adds style to chosen regions with temporal consistency
LociArt: A Unified Paradigm for Free-Style Local Video Stylization
Computer Vision and Pattern RecognitionArtificial IntelligenceMultimedia
Summary
Making videos look like paintings or other art styles usually changes the whole scene, but sometimes you want to change just parts of the video. The authors introduce a method called LociArt that lets you pick exactly where and how to change the look while keeping the rest untouched. They also created a large dataset and evaluation tools to help computers learn and judge how well this selective styling works over time. Their approach shows it can apply styles smoothly to different parts even in moving videos.
What this means in practice
- •For video production teams: Enable selective artistic effects on specific parts of videos while keeping other parts unchanged and smooth over time.$Commercial implications: Makes possible advanced editing tools for filmmakers and content creators to style portions of videos separately, improving creative control.
- •For graphic design software developers: Integrate control for applying multiple styles to different video regions within a consistent and temporally stable framework.
Authors
Yuxuan Chen, Feng Xie, Jiagao Hu, Fuhao Li, Zepeng Wang, Fei Wang, Daiguo Zhou
Abstract
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/.