mlsanityData to 14 Oct 2026

3D Pose Estimation of Two Interacting Hands from a Monocular Event Camera

arXiv 2312.14157 ↗First posted 21 Dec 2023cs.CVcited by 17

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Abstract (arXiv)

3D hand tracking from a monocular video is a very challenging problem due to hand interactions, occlusions, left-right hand ambiguity, and fast motion. Most existing methods rely on RGB inputs, which have severe limitations under low-light conditions and suffer from motion blur. In contrast, event cameras capture local brightness changes instead of full image frames and do not suffer from the described effects. Unfortunately, existing image-based techniques cannot be directly applied to events due to significant differences in the data modalities. In response to these challenges, this paper introduces the first framework for 3D tracking of two fast-moving and interacting hands from a single monocular event camera. Our approach tackles the left-right hand ambiguity with a novel semi-supervised feature-wise attention mechanism and integrates an intersection loss to fix hand collisions. To facilitate advances in this research domain, we release a new synthetic large-scale dataset of two interacting hands, Ev2Hands-S, and a new real benchmark with real event streams and ground-truth 3D annotations, Ev2Hands-R. Our approach outperforms existing methods in terms of the 3D reconstruction accuracy and generalises to real data under severe light conditions.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 49 of its 68 references that are in the map. First posted on arXiv 21 Dec 2023.

Builds on

49 of 68 references.

References · 49

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37 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 8; 2026: 5.

17 papers.

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