This paper presents a novel approach for estimating human body shape and pose from monocular images that effectively addresses the challenges of occlusions and depth ambiguity. Our proposed method BoPR, the Body-aware Part Regressor, first extracts features of both the body and part regions using an attention-guided mechanism. We then utilize these features to encode extra part-body dependency for per-part regression, with part features as queries and body feature as a reference. This allows our network to infer the spatial relationship of occluded parts with the body by leveraging visible parts and body reference information. Our method outperforms existing state-of-the-art methods on two benchmark datasets, and our experiments show that it significantly surpasses existing methods in terms of depth ambiguity and occlusion handling. These results provide strong evidence of the effectiveness of our approach.The code and data are available for research purposes at https://github.com/cyk990422/BoPR.

翻译：本文提出了一种新颖的从单目图像估计人体形状与姿态的方法，有效解决了遮挡和深度模糊带来的挑战。我们提出的方法BoPR（身体感知部位回归器）首先利用注意力引导机制提取全身及部位区域的特征。随后，我们以部位特征作为查询、全身特征作为参照，利用这些特征为每个部位的回归编码额外的部位-身体依赖关系。这使得网络能够借助可见部位和身体参考信息推断被遮挡部位与身体之间的空间关系。我们的方法在两个基准数据集上超越了现有最优方法，实验证明其在处理深度模糊和遮挡方面显著优于现有方法。这些结果为该方法有效性提供了有力证据。研究用途的代码与数据可访问 https://github.com/cyk990422/BoPR 获取。