Papers › Extract-and-Adaptation Network for 3D Interacting Hand Mesh Recovery

Extract-and-Adaptation Network for 3D Interacting Hand Mesh Recovery

5 Sep 2023arXiv:2309.01943archive 2025-07-28

JoonKyu Park, Daniel Sungho Jung, Gyeongsik Moon, Kyoung Mu Lee

Understanding how two hands interact with each other is a key component of accurate 3D interacting hand mesh recovery. However, recent Transformer-based methods struggle to learn the interaction between two hands as they directly utilize two hand features as input tokens, which results in distant token problem. The distant token problem represents that input tokens are in heterogeneous spaces, leading Transformer to fail in capturing correlation between input tokens. Previous Transformer-based methods suffer from the problem especially when poses of two hands are very different as they project features from a backbone to separate left and right hand-dedicated features. We present EANet, extract-and-adaptation network, with EABlock, the main component of our network. Rather than directly utilizing two hand features as input tokens, our EABlock utilizes two complementary types of novel tokens, SimToken and JoinToken, as input tokens. Our two novel tokens are from a combination of separated two hand features; hence, it is much more robust to the distant token problem. Using the two type of tokens, our EABlock effectively extracts interaction feature and adapts it to each hand. The proposed EANet achieves the state-of-the-art performance on 3D interacting hands benchmarks. The codes are available at https://github.com/jkpark0825/EANet.

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Tasks

3D Hand Pose Estimation3D Interacting Hand Pose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Interacting Hand Pose Estimation InterHand2.6M EANet MPJPE Test 5.88 #2 of 9 Archive leaderboard report
3D Interacting Hand Pose Estimation InterHand2.6M EANet MPVPE Test 5.45 #2 of 9 Archive leaderboard report
3D Interacting Hand Pose Estimation InterHand2.6M EANet MRRPE Test 28.54 #2 of 9 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionConcatenated Skip ConnectionDense ConnectionsDropoutLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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