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

5 Sep 2023  ·  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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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Interacting Hand Pose Estimation InterHand2.6M EANet MPJPE Test 5.88 # 1
MRRPE Test 28.54 # 1
MPVPE Test 5.45 # 1