Kinematics-based 3D Human-Object Interaction Reconstruction from Single View

• URL: http://arxiv.org/abs/2407.14043v1
• Date: Fri, 19 Jul 2024 05:44:35 GMT
• Title: Kinematics-based 3D Human-Object Interaction Reconstruction from Single View
• Authors: Yuhang Chen, Chenxing Wang,
• Abstract summary: Existing methods simply predict the body poses merely rely on network training on some indoor datasets. We propose a kinematics-based method that can drive the joints of human body to the human-object contact regions accurately.
• Score: 10.684643503514849
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstructing 3D human-object interaction (HOI) from single-view RGB images is challenging due to the absence of depth information and potential occlusions. Existing methods simply predict the body poses merely rely on network training on some indoor datasets, which cannot guarantee the rationality of the results if some body parts are invisible due to occlusions that appear easily. Inspired by the end-effector localization task in robotics, we propose a kinematics-based method that can drive the joints of human body to the human-object contact regions accurately. After an improved forward kinematics algorithm is proposed, the Multi-Layer Perceptron is introduced into the solution of inverse kinematics process to determine the poses of joints, which achieves precise results than the commonly-used numerical methods in robotics. Besides, a Contact Region Recognition Network (CRRNet) is also proposed to robustly determine the contact regions using a single-view video. Experimental results demonstrate that our method outperforms the state-of-the-art on benchmark BEHAVE. Additionally, our approach shows good portability and can be seamlessly integrated into other methods for optimizations.

Related papers

• 3D Hand Mesh Recovery from Monocular RGB in Camera Space [3.0453197258042213]
  This study proposes a network model that performs parallel processing of root-relative grids and root recovery tasks. We utilize an implicit learning approach for 2D heatmaps, enhancing the compatibility of 2D cues across different subtasks. Our proposed model is comparable with state-of-the-art models.
  arXiv  Detail & Related papers  (2024-05-12T05:36:37Z)
• Interaction-Driven Active 3D Reconstruction with Object Interiors [17.48872400701787]
  We introduce an active 3D reconstruction method which integrates visual perception, robot-object interaction, and 3D scanning. Our method operates fully automatically by a Fetch robot with built-in RGBD sensors.
  arXiv  Detail & Related papers  (2023-10-23T08:44:38Z)
• A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image [7.971699294672282]
  We present a novel method for retrieving the joint angles of a robot manipulator using only a single RGB image of its current configuration. Our approach, based on a distance-geometric representation of the configuration space, exploits the knowledge of a robot's kinematic model.
  arXiv  Detail & Related papers  (2023-01-05T12:57:45Z)
• Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps [66.24554680709417]
  Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications. We propose a non-invasive framework based on depth devices and deep neural networks to estimate the 3D pose of robots from an external camera.
  arXiv  Detail & Related papers  (2022-07-06T08:52:12Z)
• 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)
• Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition [111.87412719773889]
  We propose a joint learning framework for "interacted object localization" and "human action recognition" based on skeleton data. Our method achieves the best or competitive performance with the state-of-the-art methods for human action recognition.
  arXiv  Detail & Related papers  (2021-10-28T10:09:34Z)
• Towards unconstrained joint hand-object reconstruction from RGB videos [81.97694449736414]
  Reconstructing hand-object manipulations holds a great potential for robotics and learning from human demonstrations. We first propose a learning-free fitting approach for hand-object reconstruction which can seamlessly handle two-hand object interactions.
  arXiv  Detail & Related papers  (2021-08-16T12:26:34Z)
• Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation [89.82169646672872]
  We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori. We combine a classical geometric formulation with deep learning and extend the use of epipolar multi-rigid-body constraints to solve this task.
  arXiv  Detail & Related papers  (2020-11-30T20:46:48Z)
• Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering [53.16864661460889]
  Recent works succeed in regression-based methods which estimate parametric models directly through a deep neural network supervised by 3D ground truth. In this paper, we introduce body segmentation as critical supervision. To improve the reconstruction with part segmentation, we propose a part-level differentiable part that enables part-based models to be supervised by part segmentation.
  arXiv  Detail & Related papers  (2020-03-24T14:25:46Z)

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.