mlsanityData to 7 Oct 2026

DIREG3D: DIrectly REGress 3D Hands from Multiple Cameras

arXiv 2201.11187 ↗First posted 26 Jan 2022cs.CV cs.HC cs.ROcited by 0

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Abstract (arXiv)

In this paper, we present DIREG3D, a holistic framework for 3D Hand Tracking. The proposed framework is capable of utilizing camera intrinsic parameters, 3D geometry, intermediate 2D cues, and visual information to regress parameters for accurately representing a Hand Mesh model. Our experiments show that information like the size of the 2D hand, its distance from the optical center, and radial distortion is useful for deriving highly reliable 3D poses in camera space from just monocular information. Furthermore, we extend these results to a multi-view camera setup by fusing features from different viewpoints.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 26 Jan 2022.

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10 of 15 references.

References · 10

  1. 2019VIBE: Video Inference for Human Body Pose and Shape Estimationcited by 615
  2. 2020I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Imagecited by 274
  3. 20193D Hand Shape and Pose from Images in the Wildcited by 190
  4. 2022Embodied Hands: Modeling and Capturing Hands and Bodies Togethercited by 179
  5. 2020Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wildcited by 146
  6. 2020Epipolar Transformerscited by 117
  7. 2020Weakly Supervised 3D Hand Pose Estimation via Biomechanical Constraintscited by 97
  8. 2019CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depthcited by 75
  9. 2020Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representationcited by 57
  10. 2019Single Image 3D Hand Reconstruction with Mesh Convolutionscited by 32

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