Papers › XFormer: Fast and Accurate Monocular 3D Body Capture

XFormer: Fast and Accurate Monocular 3D Body Capture

18 May 2023arXiv:2305.11101archive 2025-07-28

Lihui Qian, Xintong Han, Faqiang Wang, Hongyu Liu, Haoye Dong, Zhiwen Li, Huawei Wei, Zhe Lin, Cheng-Bin Jin

We present XFormer, a novel human mesh and motion capture method that achieves real-time performance on consumer CPUs given only monocular images as input. The proposed network architecture contains two branches: a keypoint branch that estimates 3D human mesh vertices given 2D keypoints, and an image branch that makes predictions directly from the RGB image features. At the core of our method is a cross-modal transformer block that allows information to flow across these two branches by modeling the attention between 2D keypoint coordinates and image spatial features. Our architecture is smartly designed, which enables us to train on various types of datasets including images with 2D/3D annotations, images with 3D pseudo labels, and motion capture datasets that do not have associated images. This effectively improves the accuracy and generalization ability of our system. Built on a lightweight backbone (MobileNetV3), our method runs blazing fast (over 30fps on a single CPU core) and still yields competitive accuracy. Furthermore, with an HRNet backbone, XFormer delivers state-of-the-art performance on Huamn3.6 and 3DPW datasets.

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Tasks

3D Human Pose Estimation

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Human Pose Estimation 3DPW XFormer (HRNet) MPJPE 75 #34 of 119 Archive leaderboard report
3D Human Pose Estimation 3DPW XFormer (HRNet) MPVPE 87.1 #34 of 119 Archive leaderboard report
3D Human Pose Estimation 3DPW XFormer (HRNet) PA-MPJPE 45.7 #34 of 119 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP XFormer (HRNet) MPJPE 109.8 #75 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP XFormer (HRNet) PA-MPJPE 64.5 #75 of 108 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Batch NormalizationConvolutionHRNetReLUResidual Connection

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