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We present XFormer, a novel human mesh and motion capture method thatachieves real-time performance on consumer CPUs given only monocular images asinput. The proposed network architecture contains two branches: a keypointbranch that estimates 3D human mesh vertices given 2D keypoints, and an imagebranch that makes predictions directly from the RGB image features. At the coreof our method is a cross-modal transformer block that allows information toflow across these two branches by modeling the attention between 2D keypointcoordinates and image spatial features. Our architecture is smartly designed,which enables us to train on various types of datasets including images with2D/3D annotations, images with 3D pseudo labels, and motion capture datasetsthat do not have associated images. This effectively improves the accuracy andgeneralization 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.

XFormer: Fast and Accurate Monocular 3D Body Capture