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  • MANO
    MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand
  • InterHand2. 6M dataset | InterHand2. 6M
    Our InterHand2 6M dataset is the first large-scale real-captured dataset with accurate GT 3D interacting hand poses Specifications of InterHand2 6M are as below
  • A pytorch Implementation of MANO hand model - GitHub
    MANO is a differentiable hand model that can map hand pose parameters (joint angles and root location) and shape parameters into a 3D hand mesh The model is very realistic, has low-dimensions, and can fit any human hand
  • [2201. 02610] Embodied Hands: Modeling and Capturing Hands and Bodies . . .
    MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand
  • MANO: 3D hand model - Max Planck Institute for Intelligent Systems
    Data, code and model This includes over 1000 3D hand scans and aligned meshes, the learned 3D hand shape model, the full articulated hand model with pose-dependent blend shapes Also included is the SMPL body model with the hands attached to it, providing a realistic hand and body model
  • GitHub - lixiny manotorch: MANO hand model in PyTorch (anatomy . . .
    manotorch is a differentiable PyTorch layer that deterministically maps from pose and shape parameters to hand joints and vertices It can be integrated into any architecture as a differentiable layer to predict hand mesh manotorch is compatible with Yana's manopth package and Omid's MANO package, allowing for interchangeability between them
  • Re:InterHand Dataset | A Dataset of Relighted 3D Interacting Hands . . .
    This is an official release of Re:InterHand dataset: A Dataset of Relighted 3D Interacting Hands (NeurIPS 2023 Datasets and Benchmarks Track) Our Re:InterHand dataset has images with realistic and diverse appearances along with accurate GT 3D interacting hands
  • HaMeR Approach - GitHub Pages
    HaMeR uses a fully transformer-based network design HaMeR takes as input a single image of a hand and predicts the MANO model parameters, which are used to get the 3D hand mesh
  • 3D Hand Shape and Pose Estimation based on 2D Hand Keypoints
    We present a method for simultaneous 3D hand shape and pose estimation on a single RGB image frame Specifically, our method fits the MANO 3D hand model to 2D hand keypoints
  • GitHub - lmb-freiburg freihand: A dataset for estimation of hand pose . . .
    FreiHAND is a dataset for evaluation and training of deep neural networks for estimation of hand pose and shape from single color images, which was proposed in our paper Its current version contains 32560 unique training samples and 3960 unique evaluation samples





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