Physics-based Human Pose Estimation from a Single Moving RGB Camera

• URL: http://arxiv.org/abs/2507.17406v1
• Date: Wed, 23 Jul 2025 11:04:30 GMT
• Title: Physics-based Human Pose Estimation from a Single Moving RGB Camera
• Authors: Ayce Idil Aytekin, Chuqiao Li, Diogo Luvizon, Rishabh Dabral, Martin Oswald, Marc Habermann, Christian Theobalt,
• Abstract summary: MoviCam is the first non-synthetic dataset containing ground-truth camera trajectories.<n> PhysDynPose is a physics-based method that incorporates scene geometry and physical constraints.<n>Our method robustly estimates both human and camera poses in world coordinates.
• Score: 47.50334809388003
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most monocular and physics-based human pose tracking methods, while achieving state-of-the-art results, suffer from artifacts when the scene does not have a strictly flat ground plane or when the camera is moving. Moreover, these methods are often evaluated on in-the-wild real world videos without ground-truth data or on synthetic datasets, which fail to model the real world light transport, camera motion, and pose-induced appearance and geometry changes. To tackle these two problems, we introduce MoviCam, the first non-synthetic dataset containing ground-truth camera trajectories of a dynamically moving monocular RGB camera, scene geometry, and 3D human motion with human-scene contact labels. Additionally, we propose PhysDynPose, a physics-based method that incorporates scene geometry and physical constraints for more accurate human motion tracking in case of camera motion and non-flat scenes. More precisely, we use a state-of-the-art kinematics estimator to obtain the human pose and a robust SLAM method to capture the dynamic camera trajectory, enabling the recovery of the human pose in the world frame. We then refine the kinematic pose estimate using our scene-aware physics optimizer. From our new benchmark, we found that even state-of-the-art methods struggle with this inherently challenging setting, i.e. a moving camera and non-planar environments, while our method robustly estimates both human and camera poses in world coordinates.

Related papers

• PACE: Human and Camera Motion Estimation from in-the-wild Videos [113.76041632912577]
  We present a method to estimate human motion in a global scene from moving cameras. This is a highly challenging task due to the coupling of human and camera motions in the video. We propose a joint optimization framework that disentangles human and camera motions using both foreground human motion priors and background scene features.
  arXiv  Detail & Related papers  (2023-10-20T19:04:14Z)
• Decoupling Human and Camera Motion from Videos in the Wild [67.39432972193929]
  We propose a method to reconstruct global human trajectories from videos in the wild. Our method decouples the camera and human motion, which allows us to place people in the same world coordinate frame.
  arXiv  Detail & Related papers  (2023-02-24T18:59:15Z)
• D&D: Learning Human Dynamics from Dynamic Camera [55.60512353465175]
  We present D&D (Learning Human Dynamics from Dynamic Camera), which leverages the laws of physics to reconstruct 3D human motion from the in-the-wild videos with a moving camera. Our approach is entirely neural-based and runs without offline optimization or simulation in physics engines.
  arXiv  Detail & Related papers  (2022-09-19T06:51:02Z)
• Trajectory Optimization for Physics-Based Reconstruction of 3d Human Pose from Monocular Video [31.96672354594643]
  We focus on the task of estimating a physically plausible articulated human motion from monocular video. Existing approaches that do not consider physics often produce temporally inconsistent output with motion artifacts. We show that our approach achieves competitive results with respect to existing physics-based methods on the Human3.6M benchmark.
  arXiv  Detail & Related papers  (2022-05-24T18:02:49Z)
• GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras [99.07219478953982]
  We present an approach for 3D global human mesh recovery from monocular videos recorded with dynamic cameras. We first propose a deep generative motion infiller, which autoregressively infills the body motions of occluded humans based on visible motions. In contrast to prior work, our approach reconstructs human meshes in consistent global coordinates even with dynamic cameras.
  arXiv  Detail & Related papers  (2021-12-02T18:59:54Z)
• Contact and Human Dynamics from Monocular Video [73.47466545178396]
  Existing deep models predict 2D and 3D kinematic poses from video that are approximately accurate, but contain visible errors. We present a physics-based method for inferring 3D human motion from video sequences that takes initial 2D and 3D pose estimates as input.
  arXiv  Detail & Related papers  (2020-07-22T21:09:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.