Trajectory Optimization for Physics-Based Reconstruction of 3d Human Pose from Monocular Video

• URL: http://arxiv.org/abs/2205.12292v1
• Date: Tue, 24 May 2022 18:02:49 GMT
• Title: Trajectory Optimization for Physics-Based Reconstruction of 3d Human Pose from Monocular Video
• Authors: Erik G\"artner, Mykhaylo Andriluka, Hongyi Xu, Cristian Sminchisescu
• Abstract summary: 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.
• Score: 31.96672354594643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, while state-of-the-art physics-based approaches have either been shown to work only in controlled laboratory conditions or consider simplified body-ground contact limited to feet. This paper explores how these shortcomings can be addressed by directly incorporating a fully-featured physics engine into the pose estimation process. Given an uncontrolled, real-world scene as input, our approach estimates the ground-plane location and the dimensions of the physical body model. It then recovers the physical motion by performing trajectory optimization. The advantage of our formulation is that it readily generalizes to a variety of scenes that might have diverse ground properties and supports any form of self-contact and contact between the articulated body and scene geometry. We show that our approach achieves competitive results with respect to existing physics-based methods on the Human3.6M benchmark, while being directly applicable without re-training to more complex dynamic motions from the AIST benchmark and to uncontrolled internet videos.

Related papers

• Optimal-state Dynamics Estimation for Physics-based Human Motion Capture from Videos [6.093379844890164]
  We propose a novel method to selectively incorporate the physics models with the kinematics observations in an online setting. A recurrent neural network is introduced to realize a Kalman filter that attentively balances the kinematics input and simulated motion. The proposed approach excels in the physics-based human pose estimation task and demonstrates the physical plausibility of the predictive dynamics.
  arXiv  Detail & Related papers  (2024-10-10T10:24:59Z)
• MultiPhys: Multi-Person Physics-aware 3D Motion Estimation [28.91813849219037]
  We introduce MultiPhys, a method designed for recovering multi-person motion from monocular videos. Our focus lies in capturing coherent spatial placement between pairs of individuals across varying degrees of engagement. We devise a pipeline in which the motion estimated by a kinematic-based method is fed into a physics simulator in an autoregressive manner.
  arXiv  Detail & Related papers  (2024-04-18T08:29:29Z)
• Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening [59.88594294676711]
  Modern deep learning based motion synthesis approaches barely consider the physical plausibility of synthesized motions. We propose a system Skeleton2Humanoid'' which performs physics-oriented motion correction at test time. Experiments on the challenging LaFAN1 dataset show our system can outperform prior methods significantly in terms of both physical plausibility and accuracy.
  arXiv  Detail & Related papers  (2022-10-09T16:15:34Z)
• 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)
• Differentiable Dynamics for Articulated 3d Human Motion Reconstruction [29.683633237503116]
  We introduce DiffPhy, a differentiable physics-based model for articulated 3d human motion reconstruction from video. We validate the model by demonstrating that it can accurately reconstruct physically plausible 3d human motion from monocular video.
  arXiv  Detail & Related papers  (2022-05-24T17:58:37Z)
• Neural MoCon: Neural Motion Control for Physically Plausible Human Motion Capture [12.631678059354593]
  We exploit the high-precision and non-differentiable physics simulator to incorporate dynamical constraints in motion capture. Our key-idea is to use real physical supervisions to train a target pose distribution prior for sampling-based motion control. Results show that we can obtain physically plausible human motion with complex terrain interactions, human shape variations, and diverse behaviors.
  arXiv  Detail & Related papers  (2022-03-26T12:48:41Z)
• Learning Local Recurrent Models for Human Mesh Recovery [50.85467243778406]
  We present a new method for video mesh recovery that divides the human mesh into several local parts following the standard skeletal model. We then model the dynamics of each local part with separate recurrent models, with each model conditioned appropriately based on the known kinematic structure of the human body. This results in a structure-informed local recurrent learning architecture that can be trained in an end-to-end fashion with available annotations.
  arXiv  Detail & Related papers  (2021-07-27T14:30:33Z)
• PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time [89.68248627276955]
  Marker-less 3D motion capture from a single colour camera has seen significant progress. However, it is a very challenging and severely ill-posed problem. We present PhysCap, the first algorithm for physically plausible, real-time and marker-less human 3D motion capture.
  arXiv  Detail & Related papers  (2020-08-20T10:46:32Z)
• 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)
• Occlusion resistant learning of intuitive physics from videos [52.25308231683798]
  Key ability for artificial systems is to understand physical interactions between objects, and predict future outcomes of a situation. This ability, often referred to as intuitive physics, has recently received attention and several methods were proposed to learn these physical rules from video sequences.
  arXiv  Detail & Related papers  (2020-04-30T19:35:54Z)

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.