3D Pose Nowcasting: Forecast the Future to Improve the Present

• URL: http://arxiv.org/abs/2308.12914v2
• Date: Sat, 18 Nov 2023 15:22:04 GMT
• Title: 3D Pose Nowcasting: Forecast the Future to Improve the Present
• Authors: Alessandro Simoni, Francesco Marchetti, Guido Borghi, Federico Becattini, Lorenzo Seidenari, Roberto Vezzani, Alberto Del Bimbo
• Abstract summary: We propose a novel vision-based system leveraging depth data to accurately establish the 3D locations of skeleton joints. We introduce the concept of Pose Nowcasting, denoting the capability of the proposed system to enhance its current pose estimation accuracy. The experimental evaluation is conducted on two different datasets, providing accurate and real-time performance.
• Score: 65.65178700528747
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component useful for realizing this collaborative paradigm is the understanding of human and robot 3D poses using non-invasive systems. Therefore, in this paper, we propose a novel vision-based system leveraging depth data to accurately establish the 3D locations of skeleton joints. Specifically, we introduce the concept of Pose Nowcasting, denoting the capability of the proposed system to enhance its current pose estimation accuracy by jointly learning to forecast future poses. The experimental evaluation is conducted on two different datasets, providing accurate and real-time performance and confirming the validity of the proposed method on both the robotic and human scenarios.

Related papers

• Multi-view Pose Fusion for Occlusion-Aware 3D Human Pose Estimation [3.442372522693843]
  We present a novel approach for robust 3D human pose estimation in the context of human-robot collaboration. Our approach outperforms state-of-the-art multi-view human pose estimation techniques.
  arXiv  Detail & Related papers  (2024-08-28T14:10:57Z)
• HOIMotion: Forecasting Human Motion During Human-Object Interactions Using Egocentric 3D Object Bounding Boxes [10.237077867790612]
  We present HOIMotion, a novel approach for human motion forecasting during human-object interactions. Our method integrates information about past body poses and egocentric 3D object bounding boxes. We show that HOIMotion consistently outperforms state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2024-07-02T19:58:35Z)
• 3D WholeBody Pose Estimation based on Semantic Graph Attention Network and Distance Information [2.457872341625575]
  A novel Semantic Graph Attention Network can benefit from the ability of self-attention to capture global context. A Body Part Decoder assists in extracting and refining the information related to specific segments of the body. A Geometry Loss makes a critical constraint on the structural skeleton of the body, ensuring that the model's predictions adhere to the natural limits of human posture.
  arXiv  Detail & Related papers  (2024-06-03T10:59:00Z)
• UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues [55.69339788566899]
  UPose3D is a novel approach for multi-view 3D human pose estimation. It improves robustness and flexibility without requiring direct 3D annotations.
  arXiv  Detail & Related papers  (2024-04-23T00:18:00Z)
• Unifying Correspondence, Pose and NeRF for Pose-Free Novel View Synthesis from Stereo Pairs [57.492124844326206]
  This work delves into the task of pose-free novel view synthesis from stereo pairs, a challenging and pioneering task in 3D vision. Our innovative framework, unlike any before, seamlessly integrates 2D correspondence matching, camera pose estimation, and NeRF rendering, fostering a synergistic enhancement of these tasks.
  arXiv  Detail & Related papers  (2023-12-12T13:22:44Z)
• UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning [29.037799937729687]
  2D and 3D Human Pose Estimation (HPE) are two critical perceptual tasks in computer vision. We propose UniHPE, a unified Human Pose Estimation pipeline, which aligns features from all three modalities. Our proposed method holds immense potential to advance the field of computer vision and contribute to various applications.
  arXiv  Detail & Related papers  (2023-11-24T21:55:34Z)
• Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation [51.00725889172323]
  We propose a Pose-Oriented Transformer (POT) with uncertainty guided refinement for 3D human pose estimation. We first develop novel pose-oriented self-attention mechanism and distance-related position embedding for POT to explicitly exploit the human skeleton topology. We present an Uncertainty-Guided Refinement Network (UGRN) to refine pose predictions from POT, especially for the difficult joints.
  arXiv  Detail & Related papers  (2023-02-15T00:22:02Z)
• 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)
• Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation [70.32536356351706]
  We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations. We derive suitable measures to quantify prediction uncertainty at both pose and joint level. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-29T07:14:58Z)
• 3D Pose Estimation and Future Motion Prediction from 2D Images [26.28886209268217]
  This paper considers to jointly tackle the highly correlated tasks of estimating 3D human body poses and predicting future 3D motions from RGB image sequences. Based on Lie algebra pose representation, a novel self-projection mechanism is proposed that naturally preserves human motion kinematics.
  arXiv  Detail & Related papers  (2021-11-26T01:02:00Z)

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.