Yuxuan Chen

Local video stylization used to work in two steps: a mask marked where to edit, and a separate text prompt named the style, leaving the model to link the two. GRNStyle, built on Generative Refinement Networks, instead writes each style into the mask as a fixed polar direction of equal length, so region and style arrive as one conditioning field. Trained on 100K pairs built from global stylization, object removal, matting and compositing, it reaches a Style Prototype Similarity of 0.705 for background editing and 0.407 for foreground editing on the 168-case LociArt Benchmark, the highest in the comparison.

Local video stylization used to work in two steps: a mask marked where to edit, and a separate text prompt named the style, leaving the model to link the two. GRNStyle, built on Generative Refinement Networks, instead writes each style into the mask as a fixed polar direction of equal length, so region and style arrive as one conditioning field. Trained on 100K pairs built from global stylization, object removal, matting and compositing, it reaches a Style Prototype Similarity of 0.705 for background editing and 0.407 for foreground editing on the 168-case LociArt Benchmark, the highest in the comparison.