BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis
This provides a high-quality dataset for researchers in computer vision and robotics, addressing a bottleneck in hand pose estimation, though it is incremental as it builds on existing capture and annotation techniques.
The authors tackled the lack of large-scale, diverse hand pose datasets by introducing BigHand2.2M, collected using a novel magnetic sensor and inverse kinematics method, which significantly improves cross-benchmark performance and egocentric hand pose estimation.
In this paper we introduce a large-scale hand pose dataset, collected using a novel capture method. Existing datasets are either generated synthetically or captured using depth sensors: synthetic datasets exhibit a certain level of appearance difference from real depth images, and real datasets are limited in quantity and coverage, mainly due to the difficulty to annotate them. We propose a tracking system with six 6D magnetic sensors and inverse kinematics to automatically obtain 21-joints hand pose annotations of depth maps captured with minimal restriction on the range of motion. The capture protocol aims to fully cover the natural hand pose space. As shown in embedding plots, the new dataset exhibits a significantly wider and denser range of hand poses compared to existing benchmarks. Current state-of-the-art methods are evaluated on the dataset, and we demonstrate significant improvements in cross-benchmark performance. We also show significant improvements in egocentric hand pose estimation with a CNN trained on the new dataset.