A hand-object interaction dataset with 3D pose annotations of hand and object. The dataset contains 66,034 training images and 11,524 test images from a total of 68 sequences. The sequences are captured in multi-camera and single-camera setups and contain 10 different subjects manipulating 10 different objects from YCB dataset. The annotations are automatically obtained using an optimization algorithm. The hand pose annotations for the test set are withheld and the accuracy of the algorithms on the test set can be evaluated with standard metrics using the CodaLab challenge submission(see project page). The object pose annotations for the test and train set are provided along with the dataset.
99 PAPERS • 2 BENCHMARKS
First-Person Hand Action Benchmark is a collection of RGB-D video sequences comprised of more than 100K frames of 45 daily hand action categories, involving 26 different objects in several hand configurations.
13 PAPERS • 2 BENCHMARKS
The EgoDexter dataset provides both 2D and 3D pose annotations for 4 testing video sequences with 3190 frames. The videos are recorded with body-mounted camera from egocentric viewpoints and contain cluttered backgrounds, fast camera motion, and complex interactions with various objects. Fingertip positions were manually annotated for 1485 out of 3190 frames.
10 PAPERS • NO BENCHMARKS YET
The SynthHands dataset is a dataset for hand pose estimation which consists of real captured hand motion retargeted to a virtual hand with natural backgrounds and interactions with different objects. The dataset contains data for male and female hands, both with and without interaction with objects. While the hand and foreground object are synthtically generated using Unity, the motion was obtained from real performances as described in the accompanying paper. In addition, real object textures and background images (depth and color) were used. Ground truth 3D positions are provided for 21 keypoints of the hand.
5 PAPERS • NO BENCHMARKS YET