jyunlee / FourierHandFlow

FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow (NeurIPS 2023)

Home Page:https://jyunlee.github.io/projects/fourier-hand-flow/

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FourierHandFlow

FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow (NeurIPS 2023)

Jihyun Lee, Junbong Jang, Donghwan Kim, Minhyuk Sung, Tae-Kyun (T-K) Kim

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We present FourierHandFlow, which is a spatio-temporally continuous representation for human hands that combines a 3D occupancy field with articulation-aware query flows represented as Fourier series. Given an input RGB sequence, we aim to learn a fixed number of Fourier coefficients for each query flow to guarantee smooth and continuous temporal shape dynamics. To effectively model spatio-temporal deformations of articulated hands, we compose our 4D representation based on two types of Fourier query flow: (1) pose flow that models query dynamics influenced by hand articulation changes via implicit linear blend skinning and (2) shape flow that models query-wise displacement flow.

 

📌 Codes are updated. The instructions will be also updated shortly!

 

Citation

If you find this work useful, please consider citing our paper.

@InProceedings{lee2023fourierhandflow,
    author = {Lee, Jihyun and Jang, Junbong and Kim, Donghwan and Sung, Minhyuk and Kim, Tae-Kyun},
    title = {FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flows},
    booktitle = {NeurIPS},
    year = {2023}
}

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FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow (NeurIPS 2023)

https://jyunlee.github.io/projects/fourier-hand-flow/


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Language:Python 66.4%Language:C++ 10.9%Language:Cuda 9.3%Language:C 6.4%Language:Cython 4.6%Language:Mako 2.3%