To improve vision-based methods for understanding how people interact with their physical environment, we introduce a multi-view video and body-contact sensing dataset designed to capture dynamic human activities that involve interactions with the physical environment. The dataset includes activities such as parkour, physical training, and gym exercises, characterized by frequent body-environment contact. The proposed dataset includes 780 K images across 120 K pose sequences from 7 subjects. Each subject is captured by 6 synchronized third-person cameras, a single egocentric camera, and multiple contact sensors worn on the body. Using our proposed dataset, we benchmark state-of-the-art vision-based body contact models and show significant limitations in existing methods. Furthermore, we benchmark existing human pose estimation methods on our dataset and show that they fail under significant occlusion caused by close interactions with the environment, which indicates that our dataset can also be used to further develop pose estimation models to be more robust during interaction with the environment. To facilitate better human pose estimation from video, we introduce and evaluate a video-based human contact detection model that outperforms existing imagebased methods, underscoring the potential improvements from integrating contact information into pose estimation models. See the project page at: https://yohanshin.github.io/bodycontact4d.github.io/

Accurate three-dimensional (3D) human motion tracking is essential for immersive augmented reality$(A R)$and virtual reality (VR) applications, allowing users to engage with virtual environments through realistic full-body avatars. Achieving this level of detail, however, is challenging when the driving signals are sparse, typically coming only from upper-body sensors, such as head-mounted devices and hand controllers. To address this challenge, we propose EgoMDM (Egocentric Motion Diffusion Model), an end-to-end diffusion-based framework designed to reconstruct full-body motion from sparse tracking signals. EgoMDM models human motion in a conditional autoregressive manner using a unidirectional recurrent neural network, making it well-suited for real-time applications. By embedding local-to-global translation, forward and inverse kinematics, and foot-contact detection within the diffusion framework, EgoMDM achieves seamless, end-to-end motion synthesis, effectively reducing artifacts like foot sliding and ground penetration. Additionally, EgoMDM is conditioned on the user's body scale, allowing it to generalize across a diverse population and produce consistent avatar shapes over time. In our extensive experiments on the AMASS motion capture dataset, EgoMDM achieves state-of-the-art performance in both motion tracking accuracy and synthesis quality, demonstrating its robustness and adaptability across various human motion scenarios. Furthermore, EgoMDM significantly outperforms the existing models when tested on real signal inputs, highlighting its robustness and applicability to the real-world data. See the project page at: https://yohanshin.github.io/egomdm.github.io/

The estimation of 3D human motion from video has progressed rapidly but current methods still have several key limitations. First, most methods estimate the human in camera coordinates. Second, prior work on estimating humans in global coordinates often assumes a flat ground plane and produces foot sliding. Third, the most accurate methods rely on computationally expensive optimization pipelines, limiting their use to offline applications. Finally, existing video-based methods are surprisingly less accurate than singleframe methods. We address these limitations with WHAM (World-grounded Humans with Accurate Motion), which accurately and efficiently reconstructs 3D human motion in a global coordinate system from video. WHAM learns to lift 2D keypoint sequences to 3D using motion capture data and fuses this with video features, integrating motion context and visual information. WHAM exploits camera angular velocity estimated from a SLAM method together with human motion to estimate the body's global trajectory. We combine this with a contact-aware trajectory refinement method that lets WHAM capture human motion in diverse conditions, such as climbing stairs. WHAM outperforms all existing 3D human motion recovery methods across multiple in-the-wild benchmarks. Code is available for research purposes at http://wham.is.tue.mpg.del.

Great progress has been made in estimating 3D human pose and shape from images and video by training neural networks to directly regress the parameters of parametric human models like SMPL. However, existing body models have simplified kinematic structures that do not correspond to the true joint locations and articulations in the human skeletal system, limiting their potential use in biomechanics. On the other hand, methods for estimating biomechanically accurate skeletal motion typically rely on complex motion capture systems and expensive optimization methods. What is needed is a parametric 3D human model with a biomechanically accurate skeletal structure that can be easily posed. To that end, we develop SKEL, which re-rigs the SMPL body model with a biomechanics skeleton. To enable this, we need training data of skeletons inside SMPL meshes in diverse poses. We build such a dataset by optimizing biomechanically accurate skeletons inside SMPL meshes from AMASS sequences. We then learn a regressor from SMPL mesh vertices to the optimized joint locations and bone rotations. Finally, we re-parametrize the SMPL mesh with the new kinematic parameters. The resulting SKEL model is animatable like SMPL but with fewer, and biomechanically-realistic, degrees of freedom. We show that SKEL has more biomechanically accurate joint locations than SMPL, and the bones fit inside the body surface better than previous methods. By fitting SKEL to SMPL meshes we are able to "upgrade" existing human pose and shape datasets to include biomechanical parameters. SKEL provides a new tool to enable biomechanics in the wild, while also providing vision and graphics researchers with a better constrained and more realistic model of human articulation. The model, code, and data are available for research at https://skel.is.tue.mpg.de.