MC-hands-1M: A glove-wearing hand dataset for pose estimation

• URL: http://arxiv.org/abs/2210.10428v1
• Date: Wed, 19 Oct 2022 09:50:04 GMT
• Title: MC-hands-1M: A glove-wearing hand dataset for pose estimation
• Abstract summary: We present a synthetic dataset for 3D pose estimation of glove-wearing hands. The dataset is used to fine-tune a public hand joint detection model.
• Score: 16.83787646285556
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Nowadays, the need for large amounts of carefully and complexly annotated data for the training of computer vision modules continues to grow. Furthermore, although the research community presents state of the art solutions to many problems, there exist special cases, such as the pose estimation and tracking of a glove-wearing hand, where the general approaches tend to be unable to provide an accurate solution or fail completely. In this work, we are presenting a synthetic dataset1 for 3D pose estimation of glove-wearing hands, in order to depict the value of data synthesis in computer vision. The dataset is used to fine-tune a public hand joint detection model, achieving significant performance in both synthetic and real images of glove-wearing hands.

Related papers

• AnyHand: A Large-Scale Synthetic Dataset for RGB(-D) Hand Pose Estimation [50.62485061150716]
  We present AnyHand, a large-scale synthetic dataset designed to advance the state of the art in 3D hand pose estimation.<n>We show that extending the original training sets of existing baselines with AnyHand yields significant gains on multiple benchmarks.<n>We also contribute a lightweight depth fusion module that can be easily integrated into existing RGB-based models.
  arXiv  Detail & Related papers  (2026-03-26T17:58:54Z)
• Generalized Hand-Object Pose Estimation with Occlusion Awareness [87.10073091219066]
  Generalized 3D hand-object pose estimation from a single RGB image remains challenging due to the large variations in object appearances and interaction patterns.<n>We propose GenHOI, a framework for generalized hand-object pose estimation with occlusion awareness.
  arXiv  Detail & Related papers  (2026-03-19T15:19:23Z)
• HandMCM: Multi-modal Point Cloud-based Correspondence State Space Model for 3D Hand Pose Estimation [60.2305990057581]
  3D hand pose estimation is crucial for many human-computer interaction applications such as augmented reality.<n>HandMCM is a novel method based on the powerful state space model (Mamba)
  arXiv  Detail & Related papers  (2026-02-02T03:25:43Z)
• 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. No method can flexibly handle both scenarios and their performance degrades when applied to the other scenario. We propose UniHOPE, a unified approach for general 3D hand-object pose estimation.
  arXiv  Detail & Related papers  (2025-03-17T15:46:43Z)
• Benchmarking 2D Egocentric Hand Pose Datasets [1.611271868398988]
  Hand pose estimation from egocentric video has broad implications across various domains. This work is devoted to the analysis of state-of-the-art egocentric datasets suitable for 2D hand pose estimation.
  arXiv  Detail & Related papers  (2024-09-11T15:18:11Z)
• HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields [96.04424738803667]
  HOISDF is a guided hand-object pose estimation network. It exploits hand and object SDFs to provide a global, implicit representation over the complete reconstruction volume. We show that HOISDF achieves state-of-the-art results on hand-object pose estimation benchmarks.
  arXiv  Detail & Related papers  (2024-02-26T22:48:37Z)
• Denoising Diffusion for 3D Hand Pose Estimation from Images [38.20064386142944]
  This paper addresses the problem of 3D hand pose estimation from monocular images or sequences. We present a novel end-to-end framework for 3D hand regression that employs diffusion models that have shown excellent ability to capture the distribution of data for generative purposes. The proposed model provides state-of-the-art performance when lifting a 2D single-hand image to 3D.
  arXiv  Detail & Related papers  (2023-08-18T12:57:22Z)
• 3D Interacting Hand Pose Estimation by Hand De-occlusion and Removal [85.30756038989057]
  Estimating 3D interacting hand pose from a single RGB image is essential for understanding human actions. We propose to decompose the challenging interacting hand pose estimation task and estimate the pose of each hand separately. Experiments show that the proposed method significantly outperforms previous state-of-the-art interacting hand pose estimation approaches.
  arXiv  Detail & Related papers  (2022-07-22T13:04:06Z)
• Adversarial Motion Modelling helps Semi-supervised Hand Pose Estimation [116.07661813869196]
  We propose to combine ideas from adversarial training and motion modelling to tap into unlabeled videos. We show that an adversarial leads to better properties of the hand pose estimator via semi-supervised training on unlabeled video sequences. The main advantage of our approach is that we can make use of unpaired videos and joint sequence data both of which are much easier to attain than paired training data.
  arXiv  Detail & Related papers  (2021-06-10T17:50:19Z)
• SeqHAND:RGB-Sequence-Based 3D Hand Pose and Shape Estimation [48.456638103309544]
  3D hand pose estimation based on RGB images has been studied for a long time. We propose a novel method that generates a synthetic dataset that mimics natural human hand movements. We show that utilizing temporal information for 3D hand pose estimation significantly enhances general pose estimations.
  arXiv  Detail & Related papers  (2020-07-10T05:11:14Z)
• Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction [118.21363599332493]
  We present a method to leverage photometric consistency across time when annotations are only available for a sparse subset of frames in a video. Our model is trained end-to-end on color images to jointly reconstruct hands and objects in 3D by inferring their poses. We achieve state-of-the-art results on 3D hand-object reconstruction benchmarks and demonstrate that our approach allows us to improve the pose estimation accuracy.
  arXiv  Detail & Related papers  (2020-04-28T12:03:14Z)
• Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction [137.28465645405655]
  HANDS'19 is a challenge to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set. We show that the accuracy of state-of-the-art methods can drop, and that they fail mostly on poses absent from the training set.
  arXiv  Detail & Related papers  (2020-03-30T19:28:13Z)

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.