Egocentric human pose estimation (HPE) plays a crucial role in immersive applications such as virtual and augmented reality. However, existing methods relying on either visual or sparse inertial data alone often suffer from occlusion or ill-posed problems. In this work, we propose SAME, a novel spatial-aware multimodal fusion framework combining the complementary signals from the stereo images and sparse IMUs for accurate and robust egocentric HPE. It adopts a two-stage network based on a dual coordinate frame to mitigate the coordinate inconsistencies among the stereo cameras and the IMUs. In the first stage, the IMU signals are transformed into the local frame and iteratively fused with the stereo images for estimating 3D poses in the local frame. In the second stage, the local poses are transformed into the global frame with the 6DOF head poses provided by the head-mounted display's (HMD) SLAM algorithm and then temporally aggregated via a temporal Transformer network. Meanwhile, to achieve geometric and semantic alignment among multi-modal features, we present a depth-guided spatial-aware deformable stereo attention network and a modality-aware Transformer decoder for cross-view and cross-modal feature fusion. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on the public EMHI multi-modal egocentric pose estimation benchmark.

Existing methods for capturing multi-person holistic human motions from a monocular video usually involve integrating the detector, the tracker, and the human pose & shape estimator into a cascaded system. Differently, we develop a one-stage multi-person holistic human motion capture system, which 1) employs only one network, enabling significant benefits from the end-to-end training on a large-scale dataset; 2) enables performance improving of the tracking module during training, avoiding being limited by a pre-trained tracker; 3) captures the motions of all individuals within a single shot, rather than tracking and estimating each person sequentially. In this system, each query within a temporal cross-attention module is responsible for the long motion of a specific individual, implicitly aggregating individual-specific information throughout the entire video. To further boost the proposed system from end-to-end training, we also construct a synthetic human video dataset, with multi-person and whole-body annotations. Extensive experiments across different datasets demonstrate both the efficacy and the efficiency of both the proposed method and the dataset. Codes are avaiable at https://github.com/KenkunLiu/MaQ.