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/.
Figures & tables
Figure 1: Overview of the data construction pipeline. Source and object-removed videos are globally stylized and filtered, then combined through mask-guided matting to produce foreground- and background-stylized pairs for human verification.
Figure 2: Overview of GRNStyle. (a) Polar style-aware masks bind styles to regions and support multi-region composition. (b) Anchored mask evidence and geometric support are combined with source features through block-wise gating to guide local video stylization.
Method
Venue
SPS-fg ↑
SPS-bg ↑
CLIPT-fg ↑
CLIPT-bg ↑
PSNR-fg ↑
PSNR-bg ↑
TC ↑
Style ↑
Preserve ↑
LEAST ∗†‡ [ 35 ]
arXiv ’24
0.283
0.302
0.158
0.173
-
-
0.953
2.20
4.50
VACE-1.3B [ 14 ]
ICCV ’25
0.156
0.191
0.114
0.132
32.35
29.17
0.981
0.05
5.00
UniVST ∗‡ [ 10 ]
TPAMI ’25
0.191
0.286
0.127
0.165
27.69
23.92
0.969
1.83
4.40
RegionRoute ‡ [ 12 ]
CVPR ’26
0.317
0.491
0.154
0.183
29.38
13.17
0.955
2.41
2.75
StyleGallery ∗†‡ [ 11 ]
CVPR ’26
0.230
0.325
0.146
0.183
-
-
0.957
3.39
4.77
CoinVE-Edit [ 13 ]
arXiv ’26
0.248
0.251
0.153
0.159
20.71
16.76
0.983
3.11
3.93
Table 1: Comparison on the LociArt Benchmark. The fg/bg suffixes denote stylization of the target mask M and its complement 1−M . FT denotes local-stylization fine-tuning, and ∗ denotes training-free methods. † marks direct mask-compositing methods, for which PSNR is omitted because non-edited regions are copied from the source. ‡ marks image-based methods. Best and second-best results are shown in bold and underlined, respectively.
Figure 3: Qualitative comparison of local video stylization. Examples evaluate stylization inside the editing region, preservation outside it, and temporal consistency. Foreground and background cases highlight leakage, distortion of preserved content, and boundary inconsistency. GRNStyle produces localized styles with coherent boundaries and stable non-target content.
Table 3: Replay-free style expansion from 10 to 15 classes. Expanded models are trained only on the five new classes. Retention and acquisition are evaluated on 1,680 original-style and 840 new-style cases, respectively.
Figure 6: Data-construction ablation. For curly hair (a) and head contours (b), columns compare direct compositing, removal without matting, and the full pipeline. Rows show background- and foreground-stylized targets; insets highlight residual silhouettes, clipped contours, and boundary quality.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 7: Representative rejection cases from the data pipeline.
Figure 8: Statistics of the LociArt-100K Dataset. The 100K qualified pairs cover 18 style categories. Area ratios refer to foreground object masks Mobj , not the complementary background editing masks.
Figure 9: SPS prototype visualization and validation. (a) t-SNE visualization of 80 CSD prototypes across 18 styles. (b) Row-normalized confusion matrix under leave-one-source-out validation, achieving 93.61% macro-averaged accuracy.
Style category
Canonical phrase
in bench10
Children’s Book
children’s book illustration
✓
Claymation
claymation
✓
Comic Book
comic book
Disney
Disney animation
Ghibli
Studio Ghibli anime
✓
Japanese Anime
Japanese anime
Appendix
Table 4: Canonical style phrases for regional CLIPT. A check mark identifies the ten-style comparison subset.
Hyperparameter
Setting
Appendix
Table 5: Main training configuration for GRNStyle-2B.