1. [Publications](/publications) 2. BLADE: Single-view Body Mesh Estimation through Accurate Depth Estimation # BLADE: Single-view Body Mesh Estimation through Accurate Depth Estimation ![](/sites/default/files/publications/output_3.gif) Single-image human mesh recovery is a challenging task due to the ill-posed nature of simultaneous body shape, pose, and camera estimation. Existing estimators work well on images taken from afar, but they break down as the person moves close to the camera. Moreover, current methods fail to achieve both accurate 3D pose and 2D alignment at the same time. Error is mainly introduced by inaccurate perspective projection heuristically derived from orthographic parameters. To resolve this long-standing challenge, we present our method BLADE which accurately recovers perspective parameters from a single image without heuristic assumptions. We start from the inverse relationship between perspective distortion and the person’s Z-translation Tz, and we show that Tz can be reliably estimated from the image. We then discuss the important role of Tz for accurate human mesh recovery estimated from closerange images. Finally, we show that, once Tz and the 3D human mesh are estimated, one can accurately recover the focal length and full 3D translation. Extensive experiments on standard benchmarks and real-world close-range images show that our method accurately recovers projection parameters from a single image, and consequently attains state-of-the-art accuracy on both 3D pose estimation and 2D alignment for a wide range of images. ## Authors Shengze Wang (NVIDIA) [Jiefeng Li](/person/jiefeng-li) [Tianye Li](/person/tianye-li) [Ye Yuan](/person/ye-yuan) Henry Fuchs (UNC Chapel Hill) [Koki Nagano](/person/koki-nagano) [Shalini De Mello](/person/shalini-de-mello) [Michael Stengel](/person/michael-stengel) ## Publication Date Friday, June 13, 2025 ## Published in [IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2025](https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_BLADE_Single-view_Body_Mesh_Estimation_through_Accurate_Depth_Estimation_CVPR_2025_paper.pdf) ## Research Area [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) [Computer Graphics](/research-area/computer-graphics) [Computer Vision](/research-area/computer-vision) [Human Computer Interaction](/research-area/human-computer-interaction) [VR, AR and Display Technology](/research-area/virtual-augmented-reality) ## External Links [Code](https://github.com/NVlabs/blade) [Project Page](https://research.nvidia.com/labs/amri/projects/blade/) [ArXiv](https://arxiv.org/abs/2412.08640) ## Uploaded Files [Paper](https://d1qx31qr3h6wln.cloudfront.net/publications/Wang_BLADE_Single-view_Body_Mesh_Estimation_through_Accurate_Depth_Estimation_CVPR_2025_paper.pdf "Open file in new window")4.42 MB ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .