Half-Physics: Enabling Kinematic 3D Human Model with Physical Interactions

• URL: http://arxiv.org/abs/2507.23778v1
• Date: Thu, 31 Jul 2025 17:58:33 GMT
• Title: Half-Physics: Enabling Kinematic 3D Human Model with Physical Interactions
• Authors: Li Siyao, Yao Feng, Omid Tehari, Chen Change Loy, Michael J. Black,
• Abstract summary: We introduce a novel approach that embeds SMPL-X into a tangible entity capable of dynamic physical interactions with its surroundings.<n>Our approach maintains kinematic control over inherent SMPL-X poses while ensuring physically plausible interactions with scenes and objects.<n>Unlike reinforcement learning-based methods, which demand extensive and complex training, our half-physics method is learning-free and generalizes to any body shape and motion.
• Score: 88.01918532202716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While current general-purpose 3D human models (e.g., SMPL-X) efficiently represent accurate human shape and pose, they lacks the ability to physically interact with the environment due to the kinematic nature. As a result, kinematic-based interaction models often suffer from issues such as interpenetration and unrealistic object dynamics. To address this limitation, we introduce a novel approach that embeds SMPL-X into a tangible entity capable of dynamic physical interactions with its surroundings. Specifically, we propose a "half-physics" mechanism that transforms 3D kinematic motion into a physics simulation. Our approach maintains kinematic control over inherent SMPL-X poses while ensuring physically plausible interactions with scenes and objects, effectively eliminating penetration and unrealistic object dynamics. Unlike reinforcement learning-based methods, which demand extensive and complex training, our half-physics method is learning-free and generalizes to any body shape and motion; meanwhile, it operates in real time. Moreover, it preserves the fidelity of the original kinematic motion while seamlessly integrating physical interactions

Related papers

• Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis [51.95817740348585]
  Human-X is a novel framework designed to enable immersive and physically plausible human interactions across diverse entities.<n>Our method jointly predicts actions and reactions in real-time using an auto-regressive reaction diffusion planner.<n>Our framework is validated in real-world applications, including virtual reality interface for human-robot interaction.
  arXiv  Detail & Related papers  (2025-08-04T06:35:48Z)
• PhysiInter: Integrating Physical Mapping for High-Fidelity Human Interaction Generation [35.563978243352764]
  We introduce physical mapping, integrated throughout the human interaction generation pipeline.<n>Specifically, motion imitation within a physics-based simulation environment is used to project target motions into a physically valid space.<n>Experiments show our method achieves impressive results in generated human motion quality, with a 3%-89% improvement in physical fidelity.
  arXiv  Detail & Related papers  (2025-06-09T06:04:49Z)
• EgoGaussian: Dynamic Scene Understanding from Egocentric Video with 3D Gaussian Splatting [95.44545809256473]
  EgoGaussian is a method capable of simultaneously reconstructing 3D scenes and dynamically tracking 3D object motion from RGB egocentric input alone. We show significant improvements in terms of both dynamic object and background reconstruction quality compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-06-28T10:39:36Z)
• PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation [62.53760963292465]
  PhysDreamer is a physics-based approach that endows static 3D objects with interactive dynamics. We present our approach on diverse examples of elastic objects and evaluate the realism of the synthesized interactions through a user study.
  arXiv  Detail & Related papers  (2024-04-19T17:41:05Z)
• 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)
• 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)
• Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation [23.603254270514224]
  We propose a method for object-aware 3D egocentric pose estimation that tightly integrates kinematics modeling, dynamics modeling, and scene object information. We demonstrate for the first time, the ability to estimate physically-plausible 3D human-object interactions using a single wearable camera.
  arXiv  Detail & Related papers  (2021-06-10T17:59:50Z)
• Real-time Deep Dynamic Characters [95.5592405831368]
  We propose a deep videorealistic 3D human character model displaying highly realistic shape, motion, and dynamic appearance. We use a novel graph convolutional network architecture to enable motion-dependent deformation learning of body and clothing. We show that our model creates motion-dependent surface deformations, physically plausible dynamic clothing deformations, as well as video-realistic surface textures at a much higher level of detail than previous state of the art approaches.
  arXiv  Detail & Related papers  (2021-05-04T23:28:55Z)
• 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.