RenderMatte: Exact-Alpha Rendering and Group-Relative Alignment for Image Matting

• URL: http://arxiv.org/abs/2608.08487v1
• Date: Sun, 09 Aug 2026 05:07:56 GMT
• Title: RenderMatte: Exact-Alpha Rendering and Group-Relative Alignment for Image Matting
• Abstract summary: We present RenderMatte, a trimap-guided matting framework that adapts FLUX.1 Kontext through full- parameter fine-tuning.<n>An alpha-edge objective preserves the latent flow-matching signal while strengthening pixel-space boundary supervision.<n>Experiments show state-of-the-art performance across all benchmarks, demonstrating a scalable path toward high-fidelity matting in open-world scenes.
• Score: 11.686126175969108
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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