Abstract
Ancient murals are valuable cultural artifacts, but many have suffered severe degradation due to environmental exposure, material aging, and human activity. Restoring these artworks is challenging because it requires both reconstructing large missing structures and strictly preserving authentic, undamaged regions. This paper presents the Hybrid Mask-Aware Transformer (HMAT), a unified framework for high-fidelity mural restoration. HMAT integrates Mask-Aware Dynamic Filtering for robust local texture modeling with a Transformer bottleneck for long-range structural inference. To further address the diverse morphology of degradation, we introduce a mask-conditional style fusion module that dynamically guides the generative process. In addition, a Teacher-Forcing Decoder with hard-gated skip connections is designed to enforce fidelity in valid regions and focus reconstruction on missing areas. We evaluate HMAT on the DHMural dataset and a curated Nine-Colored Deer dataset under varying degradation levels. Experimental results demonstrate that the proposed method achieves competitive performance compared to state-of-the-art approaches, while producing more structurally coherent and visually faithful restorations. These findings suggest that HMAT provides an effective solution for the digital restoration of cultural heritage murals.
Abstract (translated)
古代壁画是珍贵的文化遗产，但许多因环境暴露、材料老化及人为活动而遭受严重退化。修复这些艺术品极具挑战性，因为既要重建大面积缺失结构，又需严格保留真实未损区域。本文提出了一种用于高保真壁画修复的统一框架——混合掩码感知Transformer（HMAT）。该框架集成了掩码感知动态滤波以 robust 建模局部纹理，并采用Transformer瓶颈进行长程结构推理。为应对多样化的退化形态，我们引入了掩码条件风格融合模块，动态引导生成过程。此外，设计了带硬门控跳跃连接的教师强制解码器，以强化有效区域的保真度并聚焦缺失区域的重建。我们在DHMural数据集及精选的《九色鹿》数据集上，针对不同退化程度评估了HMAT。实验结果表明，该方法与最先进技术相比具有竞争力，且能生成结构更连贯、视觉更忠实的修复结果。这些发现表明，HMAT为文化遗产壁画的数字修复提供了有效解决方案。
URL
https://arxiv.org/abs/2604.03984
