UniPose: Unified Cross-modality Pose Prior Propagation towards RGB-D data for Weakly Supervised 3D Human Pose Estimation

• URL: http://arxiv.org/abs/2509.23376v1
• Date: Sat, 27 Sep 2025 15:49:30 GMT
• Title: UniPose: Unified Cross-modality Pose Prior Propagation towards RGB-D data for Weakly Supervised 3D Human Pose Estimation
• Authors: Jinghong Zheng, Changlong Jiang, Jiaqi Li, Haohong Kuang, Hang Xu, Tingbing Yan,
• Abstract summary: We present UniPose, a unified cross-modality pose prior propagation method for weakly supervised 3D human pose estimation.<n>UniPose transfers 2D annotations from large-scale RGB datasets to the 3D domain via self-supervised learning.<n> Experiments on CMU Panoptic and ITOP datasets show that UniPose achieves comparable performance to fully supervised methods.
• Score: 14.52285662885727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present UniPose, a unified cross-modality pose prior propagation method for weakly supervised 3D human pose estimation (HPE) using unannotated single-view RGB-D sequences (RGB, depth, and point cloud data). UniPose transfers 2D HPE annotations from large-scale RGB datasets (e.g., MS COCO) to the 3D domain via self-supervised learning on easily acquired RGB-D sequences, eliminating the need for labor-intensive 3D keypoint annotations. This approach bridges the gap between 2D and 3D domains without suffering from issues related to multi-view camera calibration or synthetic-to-real data shifts. During training, UniPose leverages off-the-shelf 2D pose estimations as weak supervision for point cloud networks, incorporating spatial-temporal constraints like body symmetry and joint motion. The 2D-to-3D back-projection loss and cross-modality interaction further enhance this process. By treating the point cloud network's 3D HPE results as pseudo ground truth, our anchor-to-joint prediction method performs 3D lifting on RGB and depth networks, making it more robust against inaccuracies in 2D HPE results compared to state-of-the-art methods. Experiments on CMU Panoptic and ITOP datasets show that UniPose achieves comparable performance to fully supervised methods. Incorporating large-scale unlabeled data (e.g., NTU RGB+D 60) enhances its performance under challenging conditions, demonstrating its potential for practical applications. Our proposed 3D lifting method also achieves state-of-the-art results.

Related papers

• Action-Geometry Prediction with 3D Geometric Prior for Bimanual Manipulation [53.09168514034483]
  Bimanual manipulation requires policies that can reason about 3D geometry, anticipate how it evolves under action, and generate smooth, coordinated motions.<n>We propose a framework that builds bimanual manipulation directly on a pre-trained 3D geometric foundation model.<n>Our policy fuses geometry-aware latents, 2D semantic features, and proprioception into a unified state representation, and uses diffusion model to jointly predict a future action chunk and a future 3D latent that decodes into a dense pointmap.
  arXiv  Detail & Related papers  (2026-02-27T08:54:20Z)
• PandaPose: 3D Human Pose Lifting from a Single Image via Propagating 2D Pose Prior to 3D Anchor Space [62.10630827126755]
  PandaPose is a 3D human pose lifting approach via propagating 2D pose prior to 3D anchor space as the unified intermediate representation.<n>Our 3D anchor space comprises: (1) Joint-wise 3D anchors in the canonical coordinate system, providing accurate and robust priors to mitigate 2D pose estimation inaccuracies.
  arXiv  Detail & Related papers  (2026-02-01T08:20:40Z)
• xMOD: Cross-Modal Distillation for 2D/3D Multi-Object Discovery from 2D motion [4.878192303432336]
  DIOD-3D is the first baseline for multi-object discovery in 3D data using 2D motion.<n>xMOD is a cross-modal training framework that integrates 2D and 3D data while always using 2D motion cues.<n>Our approach yields a substantial performance improvement compared with the 2D object discovery state-of-the-art on all datasets.
  arXiv  Detail & Related papers  (2025-03-19T09:20:35Z)
• Towards Human-Level 3D Relative Pose Estimation: Generalizable, Training-Free, with Single Reference [65.42565481489132]
  Humans can easily deduce the relative pose of a previously unseen object, without labeling or training, given only a single query-reference image pair.<n>We propose a novel 3D generalizable relative pose estimation method by elaborating 3D/2.5D shape perception with a 2.5D shape from RGB-D reference.<n>Our differentiable takes the 2.5D rotatable mesh textured by the RGB and the semantic maps (obtained by DINOv2 from the RGB input), then renders new RGB and semantic maps under a novel rotated view.
  arXiv  Detail & Related papers  (2024-06-26T16:01:10Z)
• NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space [77.6067460464962]
  Monocular 3D Semantic Scene Completion (SSC) has garnered significant attention in recent years due to its potential to predict complex semantics and geometry shapes from a single image, requiring no 3D inputs. We identify several critical issues in current state-of-the-art methods, including the Feature Ambiguity of projected 2D features in the ray to the 3D space, the Pose Ambiguity of the 3D convolution, and the Imbalance in the 3D convolution across different depth levels. We devise a novel Normalized Device Coordinates scene completion network (NDC-Scene) that directly extends the 2
  arXiv  Detail & Related papers  (2023-09-26T02:09:52Z)
• 3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation [50.15926681475939]
  Inverse graphics aims to infer the 3D scene structure from 2D images. We introduce probabilistic modeling to quantify uncertainty and achieve robustness in 6D pose estimation tasks. 3DNEL effectively combines learned neural embeddings from RGB with depth information to improve robustness in sim-to-real 6D object pose estimation from RGB-D images.
  arXiv  Detail & Related papers  (2023-02-07T20:48:35Z)
• Unleash the Potential of Image Branch for Cross-modal 3D Object Detection [67.94357336206136]
  We present a new cross-modal 3D object detector, namely UPIDet, which aims to unleash the potential of the image branch from two aspects. First, UPIDet introduces a new 2D auxiliary task called normalized local coordinate map estimation. Second, we discover that the representational capability of the point cloud backbone can be enhanced through the gradients backpropagated from the training objectives of the image branch.
  arXiv  Detail & Related papers  (2023-01-22T08:26:58Z)
• SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation [98.83762558394345]
  SO-Pose is a framework for regressing all 6 degrees-of-freedom (6DoF) for the object pose in a cluttered environment from a single RGB image. We introduce a novel reasoning about self-occlusion, in order to establish a two-layer representation for 3D objects. Cross-layer consistencies that align correspondences, self-occlusion and 6D pose, we can further improve accuracy and robustness.
  arXiv  Detail & Related papers  (2021-08-18T19:49:29Z)
• Multi-Modality Task Cascade for 3D Object Detection [22.131228757850373]
  Many methods train two models in isolation and use simple feature concatenation to represent 3D sensor data. We propose a novel Multi-Modality Task Cascade network (MTC-RCNN) that leverages 3D box proposals to improve 2D segmentation predictions. We show that including a 2D network between two stages of 3D modules significantly improves both 2D and 3D task performance.
  arXiv  Detail & Related papers  (2021-07-08T17:55:01Z)
• RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Video [76.86512780916827]
  We present the first real-time method for motion capture of skeletal pose and 3D surface geometry of hands from a single RGB camera. In order to address the inherent depth ambiguities in RGB data, we propose a novel multi-task CNN. We experimentally verify the individual components of our RGB two-hand tracking and 3D reconstruction pipeline.
  arXiv  Detail & Related papers  (2021-06-22T12:53:56Z)
• 3D Point-to-Keypoint Voting Network for 6D Pose Estimation [8.801404171357916]
  We propose a framework for 6D pose estimation from RGB-D data based on spatial structure characteristics of 3D keypoints. The proposed method is verified on two benchmark datasets, LINEMOD and OCCLUSION LINEMOD.
  arXiv  Detail & Related papers  (2020-12-22T11:43:15Z)
• Synthetic Training for Monocular Human Mesh Recovery [100.38109761268639]
  This paper aims to estimate 3D mesh of multiple body parts with large-scale differences from a single RGB image. The main challenge is lacking training data that have complete 3D annotations of all body parts in 2D images. We propose a depth-to-scale (D2S) projection to incorporate the depth difference into the projection function to derive per-joint scale variants.
  arXiv  Detail & Related papers  (2020-10-27T03:31:35Z)

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.