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NeuralReshaper: single-image human-body retouching with deep neural networks

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References

  1. Zhou S, Fu H, Liu L, et al. Parametric reshaping of human bodies in images. ACM Trans Graph, 2010, 29: 1–10

    Article  Google Scholar 

  2. Lim J H, Ye J C. Geometric GAN. 2017. ArXiv:1705.02894

  3. Loper M, Mahmood N, Romero J, et al. SMPL: a skinned multi-person linear model. ACM Trans Graph, 2015, 34: 1–16

    Article  Google Scholar 

  4. Kanazawa A, Black M J, Jacobs D W, et al. End-to-end recovery of human shape and pose. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. 7122–7131

  5. Kolotouros N, Pavlakos G, Black M J, et al. Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. 2252–2261

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant No. 62172363).

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Correspondence to Youyi Zheng.

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Appendixes A–C. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Chen, B., Shen, Y., Fu, H. et al. NeuralReshaper: single-image human-body retouching with deep neural networks. Sci. China Inf. Sci. 66, 199101 (2023). https://doi.org/10.1007/s11432-022-3675-1

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  • DOI: https://doi.org/10.1007/s11432-022-3675-1

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