1st Place Solution of Multiview Egocentric Hand Tracking Challenge ECCV2024
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Abstract (arXiv)
Multi-view egocentric hand tracking is a challenging task and plays a critical role in VR interaction. In this report, we present a method that uses multi-view input images and camera extrinsic parameters to estimate both hand shape and pose. To reduce overfitting to the camera layout, we apply crop jittering and extrinsic parameter noise augmentation. Additionally, we propose an offline neural smoothing post-processing method to further improve the accuracy of hand position and pose. Our method achieves 13.92mm MPJPE on the Umetrack dataset and 21.66mm MPJPE on the HOT3D dataset.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 11 references that are in the map. First posted on arXiv 28 Sep 2024.
Builds on
9 of 11 references.
References · 9
- 2022ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulationcited by 312
- 2023Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistlescited by 290
- 2023AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimationcited by 64
- 2020Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representationcited by 57
- 2022UmeTrack: Unified multi-view end-to-end hand tracking for VRcited by 50
- 2024Introducing HOT3D: An Egocentric Dataset for 3D Hand and Object Trackingcited by 48
- 2024Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objectscited by 35
- 2024A Simple Baseline for Efficient Hand Mesh Reconstructioncited by 27
- 20231st Place Solution of Egocentric 3D Hand Pose Estimation Challenge 2023 Technical Report:A Concise Pipeline for Egocentric Hand Pose Reconstructioncited by 3
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Citing papers per year: 2026: 1.
1 paper.