Human de-occlusion, which aims to infer the appearance of invisible human parts from an occluded image, has great value in many human-related tasks, such as person re-id, and intention inference. To address this task, this paper proposes a dynamic mask-aware transformer (DMAT), which dynamically augments information from human regions and weakens that from occlusion. First, to enhance token representation, we design an expanded convolution head with enlarged kernels, which captures more local valid context and mitigates the influence of surrounding occlusion. To concentrate on the visible human parts, we propose a novel dynamic multi-head human-mask guided attention mechanism through integrating multiple masks, which can prevent the de-occluded regions from assimilating to the background. Besides, a region upsampling strategy is utilized to alleviate the impact of occlusion on interpolated images. During model learning, an amodal loss is developed to further emphasize the recovery effect of human regions, which also refines the model's convergence. Extensive experiments on the AHP dataset demonstrate its superior performance compared to recent state-of-the-art methods.

本文提出了一种动态掩蔽感知变换器（DMAT），用于从遮挡图像中推断不可见的人体部分的外观，解决了人体去遮挡任务，对于人员重新识别和意图推断等与人类相关的任务具有巨大价值。通过设计扩展的卷积头部，并整合多个掩蔽蒙版，提出新颖的动态多头人掩蔽引导注意机制，强调可见的人体部分，利用区域上采样策略减轻插值图像受遮挡的影响，并引入无模损失进行学习，在AHP数据集上的广泛实验表明其优于最新的最先进方法。

DMAT：一种用于人体去遮挡的动态掩蔽感知变压器