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MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints
The MS-MANO model allows for movements that are more human-like and physiologically realistic and bridges the gap between image observations to biomechanics. -
Latent regression forest: Structured estimation of 3D articulated hand posture
Latent regression forest: Structured estimation of 3D articulated hand posture. -
REGION ENSEMBLE NETWORK: IMPROVING CONVOLUTIONAL NETWORK FOR HAND POSE ESTIMA...
Hand pose estimation from monocular depth images is an important and challenging problem for human-computer interaction. Recently deep convolutional networks (ConvNet) with... -
NYU Hand Pose Dataset
The NYU Hand Pose Dataset comprises 70,000 images captured with a depth sensor in VGA resolution accompanied by ground truth annotations of positions of hand joints. -
Stereo-based Hand Tracking Benchmark
A benchmark for evaluating hand pose tracking/estimation algorithms on passive stereo. Unlike existing benchmarks, it contains both stereo images from a binocular stereo camera... -
Bighand2.2m Benchmark
Bighand2.2m benchmark: Hand pose dataset and state of the art analysis. -
Re:InterHand
Re:InterHand dataset is a large-scale dataset for 3D interacting hand pose estimation from a single RGB image. -
InterHand2.6M
The InterHand2.6M dataset contains 366K training samples, 110K validation samples, and 261K test samples. It is the only interacting two-hand dataset with dense shape annotations. -
Parallel mesh reconstruction streams for pose estimation of interacting hands
A new multi-stream 3D mesh reconstruction network (MSMR-Net) for hand pose estimation from a single RGB image.
