Abstract
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.
Abstract (translated)
本论文提出了一种从单视角图像中估算人体形状和姿态的新方法,能够有效解决遮挡和深度不确定性的挑战。我们提出的算法是Body-aware Part Regressor,它使用注意力引导机制从身体和部分区域中提取特征。随后,我们利用这些特征为每个部分生成额外的身体-部分依赖关系,以作为查询和参考。这使我们的网络可以利用可见部分和身体参考信息推断遮挡部分与身体的空间关系。我们的方法在两个基准数据集上比现有最先进的方法表现更好,我们的实验表明它在深度不确定性和遮挡处理方面 significantly 超过了现有方法。这些结果提供了我们方法有效性的强烈证据。
URL
https://arxiv.org/abs/2303.11675
