TopoColor: Topology-Aware Region Correspondence for Line Art Colorization.

Abstract

Line art colorization is a critical yet labor-intensive bottleneck in professional animation production, requiring artists to maintain rigorous color consistency across frames. While contemporary methods leveraging feature alignment or generative diffusion have improved efficiency, they often fail when confronted with occlusions and large inter-frame deformations. The fundamental difficulty arises from the inherent sparsity of binary line-art images: the absence of internal texture limits the discriminative power of local feature descriptors, resulting in ambiguous region correspondences during propagation. To address these limitations, we propose a region-based feature enhancement framework built upon a Topology-aware Segment Graph (TSG). By explicitly modeling topological adjacency-a robust structural cue that remains invariant across frames despite significant geometric changes-our method leverages spatial context to increase feature separability across semantically distinct regions, enabling more robust alignment. Furthermore, to mitigate temporal drift and the diminishing relevance of static reference frames in long sequences, we introduce a Dual-source Temporal Selection and Refinement strategy. This mechanism adaptively integrates information from both the global reference keyframe and the immediate previous frame, balancing color fidelity with spatial continuity. Extensive experiments on industrial benchmarks demonstrate that our approach achieves superior color accuracy and temporal stability compared to state-of-the-art frameworks, particularly in scenarios involving complex character motion and topological variation.