BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis (1704.02612v2)

Abstract: 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.

• The paper introduces a novel hand pose dataset employing magnetic sensors and inverse kinematics for accurate 21-joint annotations.
• It demonstrates enhanced data quality with mean estimation errors as low as 15-20mm, outperforming existing benchmarks.
• The dataset supports robust model training and paves the way for innovations in VR, HCI, and sign language recognition.

Overview of the BigHand2.2M Benchmark Paper

This paper presents the BigHand2.2M dataset, which addresses limitations in existing hand pose datasets regarding scale, annotation accuracy, and hand pose diversity. The authors introduce an advanced capture technique employing magnetic sensors coupled with inverse kinematics for efficient and accurate 21-joint hand pose annotations. This methodological innovation allows for the large-scale collection of annotated depth maps, advancing data quality and coverage compared to previous efforts in hand pose estimation.

Methodology

The dataset is captured using a system composed of six 6D magnetic sensors and a depth sensor, alongside a novel protocol to maximize the coverage of the hand's natural pose space. Participants in the paper performed a series of predefined and random hand movements, ensuring that both articulation and global hand orientation are thoroughly sampled and represented. The inclusion of 290K egocentric frames further distinguishes this dataset, allowing for enhanced research in egocentric hand pose estimation.

Results

Empirical evaluations leveraged the increased scale and precision of the BigHand2.2M dataset. Training a CNN on this dataset showed superior or comparable performance on existing benchmarks like NYU and ICVL even without task-specific tuning. The paper reports mean estimation errors as low as 15-20mm on real datasets, demonstrating marked improvements in model generalization across different benchmarks. This evidence underscores the utility of a comprehensive, well-annotated dataset in enhancing the capabilities of deep learning-based hand pose estimation models.

Implications

The BigHand2.2M dataset holds substantial implications for hand pose estimation research. It not only facilitates more rigorous evaluation methodologies but also supports the development of models with improved generalization capabilities. This dataset offers a new standard for future model training and could spur innovations in real-time applications such as virtual reality, human-computer interaction, and sign language recognition. The implications extend beyond practical applications, inviting theoretical advancements by allowing exploration into the scalability of deep learning architectures with increased training data diversity.

Future Directions

Future work may explore extending this dataset by integrating additional sensors or modalities to encompass more complex scenarios of hand articulation. Another trajectory involves leveraging augmented reality to synthesize additional data. The dataset can serve as a basis for comparative studies to interrogate performance discrepancies among models trained on synthetic versus real data. Furthermore, the application of transfer learning to bridge gaps between synthetic data distributions and real-world scenarios remains an open field for exploration.

In conclusion, the BigHand2.2M paper significantly contributes to hand pose estimation by delivering a rich, meticulously annotated dataset and setting a high benchmark for future research. This advancement paves the way for new developments in enhancing model robustness, extending applications in varied contextual settings, and setting deeper inquiries within the realms of machine perception.