Abstract
Lineart colorization is a critical stage in professional content creation, yet achieving precise and flexible results under diverse user constraints remains a significant challenge. To address this, we propose OmniColor, a unified framework for multi-modal lineart colorization that supports arbitrary combinations of control signals. Specifically, we systematically categorize guidance signals into two types: spatially-aligned conditions and semantic-reference conditions. For spatially-aligned inputs, we employ a dual-path encoding strategy paired with a Dense Feature Alignment loss to ensure rigorous boundary preservation and precise color restoration. For semantic-reference inputs, we utilize a VLM-only encoding scheme integrated with a Temporal Redundancy Elimination mechanism to filter repetitive information and enhance inference efficiency. To resolve potential input conflicts, we introduce an Adaptive Spatial-Semantic Gating module that dynamically balances multi-modal constraints. Experimental results demonstrate that OmniColor achieves superior controllability, visual quality, and temporal stability, providing a robust and practical solution for lineart colorization. The source code and dataset will be open at this https URL.
Abstract (translated)
线稿上色是专业内容创作中的关键环节，但在多样化的用户约束条件下实现精确且灵活的结果仍是一项重大挑战。为此，我们提出了OmniColor，一个支持任意控制信号组合的统一多模态线稿上色框架。具体而言，我们系统地将引导信号分为两类：空间对齐条件与语义参考条件。对于空间对齐输入，我们采用双路径编码策略并结合密集特征对齐损失，以确保严格的边界保持与精确的色彩还原。对于语义参考输入，我们使用纯视觉语言模型编码方案，并集成时间冗余消除机制以过滤重复信息、提升推理效率。为解决潜在的输入冲突，我们引入了自适应空间-语义门控模块，以动态平衡多模态约束。实验结果表明，OmniColor在可控性、视觉质量与时序稳定性上均达到优异表现，为线稿上色提供了稳健实用的解决方案。源代码与数据集将在以下链接公开：https://URL。
URL
https://arxiv.org/abs/2603.27531
