AI 中文总结
本文提出RenderMatte框架，通过微调FLUX.1 Kontext实现精确Alpha渲染，引入组相对Alpha对齐优化，并构建专属数据集，在抠图基准测试中取得最优性能，解决开放世界场景的抠图难题。
AI 中文摘要
图像抠图是现代视觉内容制作的关键支撑技术，前景提取决定了下游创作工作流的真实感与可编辑性。然而，开放世界场景中的精确Alpha估计仍具挑战性，因为真实前景呈现出高度多样的外观与不透明度模式，这使得现有方法在语义模糊性和细粒度不透明度变化上表现不佳，尤其是在脆弱且难以监督的稀疏边界区域。为解决这一问题，我们提出RenderMatte，这是一个由trimap引导的抠图框架，通过全参数微调适配FLUX.1 Kontext，利用图像编辑先验实现结构保留的Alpha预测。在监督适配过程中，Alpha边缘目标保留潜在流匹配信号，同时强化像素空间的边界监督。我们进一步引入用于训练后处理的组相对Alpha对齐，该方法通过抠图特定奖励（涵盖Alpha精度、边界保真度、trimap符合性与合成一致性）比较相同trimap条件下采样得到的多个抠图结果。为克服精确边缘标注的缺失，我们构建了RenderMatte数据集，这是一个大规模合成数据集，结合了3D渲染的RGBA前景与多样的多源资产，具有精确的发丝级Alpha标注和多样的背景合成。实验结果显示，该方法在所有基准测试中均达到了当前最优性能，为开放世界场景中的高保真抠图提供了可扩展的实现路径。
英文摘要
Image matting is an essential enabling technology for modern visual content production, where foreground extraction determines the realism and editability of downstream creation workflows. However, precise alpha estimation in open-world scenes remains challenging because real foregrounds exhibit highly diverse appearances and opacity patterns. This makes existing methods struggle with semantic ambiguity and fine-grained opacity variation, especially in sparse boundary regions that are fragile and difficult to supervise. To address this gap, we present RenderMatte, a trimap-guided matting framework that adapts FLUX.1 Kontext through full-parameter fine-tuning, leveraging image editing priors for structure-preserving alpha prediction. During supervised adaptation, an alpha-edge objective preserves the latent flow-matching signal while strengthening pixel-space boundary supervision. We further introduce group-relative alpha alignment for post-training. It compares multiple mattes sampled under the same trimap condition using matting-specific rewards for alpha accuracy, boundary fidelity, trimap compliance, and compositional consistency. To overcome the lack of precise edge annotations, we construct the RenderMatte dataset, a large-scale synthetic dataset combining 3D-rendered RGBA foregrounds with diverse multi-source assets. It features exact strand-level alpha annotations and diverse background composites. Experiments show state-of-the-art performance across all benchmarks, demonstrating a scalable path toward high-fidelity matting in open-world scenes.
