Human mesh reconstruction from a single image is challenging in the presence of occlusion, which can be caused by self, objects, or other humans. Existing methods either fail to separate human features accurately or lack proper supervision for feature completion. In this paper, we propose Dense Inpainting Human Mesh Recovery (DIMR), a two-stage method that leverages dense correspondence maps to handle occlusion. Our method utilizes a dense correspondence map to separate visible human features and completes human features on a structured UV map dense human with an attention-based feature completion module. We also design a feature inpainting training procedure that guides the network to learn from unoccluded features. We evaluate our method on several datasets and demonstrate its superior performance under heavily occluded scenarios compared to other methods. Extensive experiments show that our method obviously outperforms prior SOTA methods on heavily occluded images and achieves comparable results on the standard benchmarks (3DPW).

翻译：单张图像中的人体网格重建在遮挡（由自身、物体或其他人体引起）场景下极具挑战性。现有方法要么无法准确分离人体特征，要么缺乏对特征补全的恰当监督。本文提出密集修复人体网格重建（DIMR），一种利用密集对应图处理遮挡的两阶段方法。该方法通过密集对应图分离可见人体特征，并借助基于注意力的特征补全模块，在结构化UV密集人体图中完成人体特征补全。我们同时设计了特征修复训练流程，引导网络从无遮挡特征中学习。在多个数据集上的评估表明，该方法在严重遮挡场景下性能显著优于现有方法。大量实验证明，本方法在重度遮挡图像上明显超越先前SOTA方法，并在标准基准（3DPW）上取得可比较的结果。