VPHO: Joint Visual-Physical Cue Learning and Aggregation for Hand-Object Pose Estimation

• URL: http://arxiv.org/abs/2511.12030v1
• Date: Sat, 15 Nov 2025 04:47:30 GMT
• Title: VPHO: Joint Visual-Physical Cue Learning and Aggregation for Hand-Object Pose Estimation
• Authors: Jun Zhou, Chi Xu, Kaifeng Tang, Yuting Ge, Tingrui Guo, Li Cheng,
• Abstract summary: Estimating the 3D poses of hands and objects from a single RGB image is a fundamental yet challenging problem.<n>We propose a novel framework that jointly integrates visual and physical cues for hand-object pose estimation.<n>Our method significantly outperforms existing state-of-the-art approaches in both pose accuracy and physical plausibility.
• Score: 12.959132766584425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating the 3D poses of hands and objects from a single RGB image is a fundamental yet challenging problem, with broad applications in augmented reality and human-computer interaction. Existing methods largely rely on visual cues alone, often producing results that violate physical constraints such as interpenetration or non-contact. Recent efforts to incorporate physics reasoning typically depend on post-optimization or non-differentiable physics engines, which compromise visual consistency and end-to-end trainability. To overcome these limitations, we propose a novel framework that jointly integrates visual and physical cues for hand-object pose estimation. This integration is achieved through two key ideas: 1) joint visual-physical cue learning: The model is trained to extract 2D visual cues and 3D physical cues, thereby enabling more comprehensive representation learning for hand-object interactions; 2) candidate pose aggregation: A novel refinement process that aggregates multiple diffusion-generated candidate poses by leveraging both visual and physical predictions, yielding a final estimate that is visually consistent and physically plausible. Extensive experiments demonstrate that our method significantly outperforms existing state-of-the-art approaches in both pose accuracy and physical plausibility.

Related papers

• PhysHMR: Learning Humanoid Control Policies from Vision for Physically Plausible Human Motion Reconstruction [52.44375492811009]
  We present PhysHMR, a unified framework that learns a visual-to-action policy for humanoid control in a physics-based simulator.<n>A key component of our approach is the pixel-as-ray strategy, which lifts 2D keypoints into 3D spatial rays and transforms them into global space.<n>PhysHMR produces high-fidelity, physically plausible motion across diverse scenarios, outperforming prior approaches in both visual accuracy and physical realism.
  arXiv  Detail & Related papers  (2025-10-02T21:01:11Z)
• SIGHT: Synthesizing Image-Text Conditioned and Geometry-Guided 3D Hand-Object Trajectories [124.24041272390954]
  Modeling hand-object interaction priors holds significant potential to advance robotic and embodied AI systems.<n>We introduce SIGHT, a novel task focused on generating realistic and physically plausible 3D hand-object interaction trajectories from a single image.<n>We propose SIGHT-Fusion, a novel diffusion-based image-text conditioned generative model that tackles this task by retrieving the most similar 3D object mesh from a database.
  arXiv  Detail & Related papers  (2025-03-28T20:53:20Z)
• Zero-Shot Human-Object Interaction Synthesis with Multimodal Priors [31.277540988829976]
  This paper proposes a novel zero-shot HOI synthesis framework without relying on end-to-end training on currently limited 3D HOI datasets.<n>We employ pre-trained human pose estimation models to extract human poses and introduce a generalizable category-level 6-DoF estimation method to obtain the object poses from 2D HOI images.
  arXiv  Detail & Related papers  (2025-03-25T23:55:47Z)
• UniHOPE: A Unified Approach for Hand-Only and Hand-Object Pose Estimation [82.93208597526503]
  Existing methods are specialized, focusing on either bare-hand or hand interacting with object.<n>No method can flexibly handle both scenarios and their performance degrades when applied to the other scenario.<n>We propose UniHOPE, a unified approach for general 3D hand-object pose estimation.
  arXiv  Detail & Related papers  (2025-03-17T15:46:43Z)
• 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)
• Closely Interactive Human Reconstruction with Proxemics and Physics-Guided Adaption [64.07607726562841]
  Existing multi-person human reconstruction approaches mainly focus on recovering accurate poses or avoiding penetration. In this work, we tackle the task of reconstructing closely interactive humans from a monocular video. We propose to leverage knowledge from proxemic behavior and physics to compensate the lack of visual information.
  arXiv  Detail & Related papers  (2024-04-17T11:55:45Z)
• Self-supervised Neural Articulated Shape and Appearance Models [18.99030452836038]
  We propose a novel approach for learning a representation of the geometry, appearance, and motion of a class of articulated objects. Our representation learns shape, appearance, and articulation codes that enable independent control of these semantic dimensions.
  arXiv  Detail & Related papers  (2022-05-17T17:50:47Z)
• LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies [78.17425779503047]
  We propose a novel neural implicit representation for the human body. It is fully differentiable and optimizable with disentangled shape and pose latent spaces. Our model can be trained and fine-tuned directly on non-watertight raw data with well-designed losses.
  arXiv  Detail & Related papers  (2021-11-30T04:10:57Z)
• Physion: Evaluating Physical Prediction from Vision in Humans and Machines [46.19008633309041]
  We present a visual and physical prediction benchmark that precisely measures this capability. We compare an array of algorithms on their ability to make diverse physical predictions. We find that graph neural networks with access to the physical state best capture human behavior.
  arXiv  Detail & Related papers  (2021-06-15T16:13:39Z)
• 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.