We address the challenge of accurate 3D human pose and shape estimation from monocular images. The key to accuracy and robustness lies in high-quality training data. Existing training datasets containing real images with pseudo ground truth (pGT) use SMPLify to fit SMPL to sparse 2D joint locations, assuming a simplified camera with default intrinsics. We make two contributions that improve pGT accuracy. First, to estimate camera intrinsics, we develop a field-of-view prediction model (HumanFoV) trained on a dataset of images containing people. We use the estimated intrinsics to enhance the 4D-Humans dataset by incorporating a full perspective camera model during SMPLify fitting. Second, 2D joints provide limited constraints on 3D body shape, resulting in average-looking bodies. To address this, we use the BEDLAM dataset to train a dense surface keypoint detector. We apply this detector to the 4D-Humans dataset and modify SMPLify to fit the detected keypoints, resulting in significantly more realistic body shapes. Finally, we upgrade the HMR2.0 architecture to include the estimated camera parameters. We iterate model training and SMPLify fitting initialized with the previously trained model. This leads to more accurate pGT and a new model, CameraHMR, with state-of-the-art accuracy. Code and pGT are available for research purposes.

本研究解决了从单目图像中精确估计3D人体姿态和形状的挑战，前提是高质量的训练数据。我们提出了一种基于视野预测模型的相机内参估计方法，并结合密集表面关键点检测器，提高了SMPLify的拟合精度，显著改善了人体形状的真实感，最终提出了状态最先进的模型CameraHMR。

CameraHMR: 透视下的人体对齐