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Layered-Garment Net: Generating Multiple Implicit Garment Layers from a Single Image

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13841))

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Abstract

Recent research works have focused on generating human models and garments from their 2D images. However, state-of-the-art researches focus either on only a single layer of the garment on a human model or on generating multiple garment layers without any guarantee of the intersection-free geometric relationship between them. In reality, people wear multiple layers of garments in their daily life, where an inner layer of garment could be partially covered by an outer one. In this paper, we try to address this multi-layer modeling problem and propose the Layered-Garment Net (LGN) that is capable of generating intersection-free multiple layers of garments defined by implicit function fields over the body surface, given the person’s near front-view image. With a special design of garment indication fields (GIF), we can enforce an implicit covering relationship between the signed distance fields (SDF) of different layers to avoid self-intersections among different garment surfaces and the human body. Experiments demonstrate the strength of our proposed LGN framework in generating multi-layer garments as compared to state-of-the-art methods. To the best of our knowledge, LGN is the first research work to generate intersection-free multiple layers of garments on the human body from a single image.

A. Aggarwal, J. Wang, S. Hogue and X. Guo are partially supported by National Science Foundation (OAC-2007661).

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References

  1. Bhatnagar, B.L., Tiwari, G., Theobalt, C., Pons-Moll, G.: Multi-garment net: Learning to dress 3d people from images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5420–5430 (2019)

    Google Scholar 

  2. Jiang, B., Zhang, J., Hong, Y., Luo, J., Liu, L., Bao, H.: BCNet: learning body and cloth shape from a single image. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 18–35. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58565-5_2

    Chapter  Google Scholar 

  3. Patel, C., Liao, Z., Pons-Moll, G.: TailorNet: predicting clothing in 3D as a function of human pose, shape and garment style. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7365–7375 (2020)

    Google Scholar 

  4. Su, Z., Yu, T., Wang, Y., Li, Y., Liu, Y.: Deepcloth: neural garment representation for shape and style editing. arXiv preprint arXiv:2011.14619 (2020)

  5. Corona, E., Pumarola, A., Alenya, G., Pons-Moll, G., Moreno-Noguer, F.: Smplicit: topology-aware generative model for clothed people. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11875–11885 (2021)

    Google Scholar 

  6. Alldieck, T., Magnor, M., Xu, W., Theobalt, C., Pons-Moll, G.: Video based reconstruction of 3d people models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8387–8397 (2018)

    Google Scholar 

  7. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graphics (TOG) 34, 1–16 (2015)

    Article  Google Scholar 

  8. Pavlakos, G., et al.: Expressive body capture: 3D hands, face, and body from a single image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10975–10985 (2019)

    Google Scholar 

  9. Osman, A.A.A., Bolkart, T., Black, M.J.: STAR: sparse trained articulated human body regressor. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 598–613. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58539-6_36

    Chapter  Google Scholar 

  10. Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J.: Keep it SMPL: automatic estimation of 3D human pose and shape from a single image. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 561–578. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_34

    Chapter  Google Scholar 

  11. Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7122–7131 (2018)

    Google Scholar 

  12. Pavlakos, G., Zhu, L., Zhou, X., Daniilidis, K.: Learning to estimate 3D human pose and shape from a single color image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern recognition, pp. 459–468 (2018)

    Google Scholar 

  13. Smith, B.M., Chari, V., Agrawal, A., Rehg, J.M., Sever, R.: Towards accurate 3D human body reconstruction from silhouettes. In: 2019 International Conference on 3D Vision (3DV), pp. 279–288. IEEE (2019)

    Google Scholar 

  14. Omran, M., Lassner, C., Pons-Moll, G., Gehler, P., Schiele, B.: Neural body fitting: Unifying deep learning and model based human pose and shape estimation. In: 2018 international conference on 3D vision (3DV), pp. 484–494. IEEE (2018)

    Google Scholar 

  15. Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M.J., Gehler, P.V.: Unite the people: closing the loop between 3D and 2D human representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6050–6059 (2017)

    Google Scholar 

  16. Alldieck, T., Magnor, M., Bhatnagar, B.L., Theobalt, C., Pons-Moll, G.: Learning to reconstruct people in clothing from a single RGB camera. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1175–1186 (2019)

    Google Scholar 

  17. Alldieck, T., Pons-Moll, G., Theobalt, C., Magnor, M.: Tex2shape: detailed full human body geometry from a single image. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2293–2303 (2019)

    Google Scholar 

  18. Su, Z., et al.: Mulaycap: multi-layer human performance capture using a monocular video camera. arXiv preprint arXiv:2004.05815 (2020)

  19. Varol, G., et al.: BodyNet: volumetric inference of 3D human body shapes. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 20–38. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_2

    Chapter  Google Scholar 

  20. Zheng, Z., Yu, T., Wei, Y., Dai, Q., Liu, Y.: Deephuman: 3D human reconstruction from a single image. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7739–7749 (2019)

    Google Scholar 

  21. Ma, Q., Saito, S., Yang, J., Tang, S., Black, M.J.: Scale: Modeling clothed humans with a surface codec of articulated local elements. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16082–16093 (2021)

    Google Scholar 

  22. Ma, Q., et al.: Learning to dress 3D people in generative clothing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2020)

    Google Scholar 

  23. Wang, L., Zhao, X., Yu, T., Wang, S., Liu, Y.: NormalGAN: learning detailed 3D human from a single RGB-D image. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 430–446. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58565-5_26

    Chapter  Google Scholar 

  24. Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3D reconstruction in function space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4460–4470 (2019)

    Google Scholar 

  25. Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)

  26. Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. ACM SIGGRAPH Comput. Graphics 21, 163–169 (1987)

    Article  Google Scholar 

  27. Chibane, J., Mir, A., Pons-Moll, G.: Neural unsigned distance fields for implicit function learning. arXiv preprint arXiv:2010.13938 (2020)

  28. Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 165–174 (2019)

    Google Scholar 

  29. Xu, Q., Wang, W., Ceylan, D., Mech, R., Neumann, U.: DISN: deep implicit surface network for high-quality single-view 3D reconstruction. arXiv preprint arXiv:1905.10711 (2019)

  30. Sitzmann, V., Chan, E.R., Tucker, R., Snavely, N., Wetzstein, G.: MetaSDF: meta-learning signed distance functions. arXiv preprint arXiv:2006.09662 (2020)

  31. Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., Li, H.: PIFU: pixel-aligned implicit function for high-resolution clothed human digitization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2304–2314 (2019)

    Google Scholar 

  32. Saito, S., Simon, T., Saragih, J., Joo, H.: Pifuhd: multi-level pixel-aligned implicit function for high-resolution 3D human digitization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 84–93 (2020)

    Google Scholar 

  33. Hong, Y., Zhang, J., Jiang, B., Guo, Y., Liu, L., Bao, H.: StereoPIFU: depth aware clothed human digitization via stereo vision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 535–545 (2021)

    Google Scholar 

  34. He, T., Collomosse, J., Jin, H., Soatto, S.: Geo-PIFU: geometry and pixel aligned implicit functions for single-view human reconstruction. arXiv preprint arXiv:2006.08072 (2020)

  35. Zheng, Z., Yu, T., Liu, Y., Dai, Q.: Pamir: parametric model-conditioned implicit representation for image-based human reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)

    Google Scholar 

  36. Wang, S., Mihajlovic, M., Ma, Q., Geiger, A., Tang, S.: MetaAvatar: learning animatable clothed human models from few depth images. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)

    Google Scholar 

  37. Huang, Z., Xu, Y., Lassner, C., Li, H., Tung, T.: Arch: animatable reconstruction of clothed humans. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3093–3102 (2020)

    Google Scholar 

  38. Peng, S., et al.: Neural body: implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9054–9063 (2021)

    Google Scholar 

  39. Peng, S., et al.: Animatable neural radiance fields for modeling dynamic human bodies. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14314–14323 (2021)

    Google Scholar 

  40. Liu, L., Habermann, M., Rudnev, V., Sarkar, K., Gu, J., Theobalt, C.: Neural actor: neural free-view synthesis of human actors with pose control. arXiv preprint arXiv:2106.02019 (2021)

  41. Shao, R., Zhang, H., Zhang, H., Cao, Y., Yu, T., Liu, Y.: Doublefield: bridging the neural surface and radiance fields for high-fidelity human rendering. arXiv preprint arXiv:2106.03798 (2021)

  42. Saito, S., Yang, J., Ma, Q., Black, M.J.: Scanimate: weakly supervised learning of skinned clothed avatar networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2886–2897 (2021)

    Google Scholar 

  43. Bhatnagar, B.L., Sminchisescu, C., Theobalt, C., Pons-Moll, G.: Combining implicit function learning and parametric models for 3D human reconstruction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 311–329. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58536-5_19

    Chapter  Google Scholar 

  44. Gropp, A., Yariv, L., Haim, N., Atzmon, M., Lipman, Y.: Implicit geometric regularization for learning shapes. arXiv preprint arXiv:2002.10099 (2020)

  45. Tiwari, G., Bhatnagar, B.L., Tung, T., Pons-Moll, G.: SIZER: a dataset and model for parsing 3D clothing and learning size sensitive 3D clothing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 1–18. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_1

    Chapter  Google Scholar 

  46. Zhu, H., et al.: Deep Fashion3D: a dataset and benchmark for 3D garment reconstruction from single images. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 512–530. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_30

    Chapter  Google Scholar 

  47. Chi, C., Song, S.: Garmentnets: category-level pose estimation for garments via canonical space shape completion. arXiv preprint arXiv:2104.05177 (2021)

  48. Jacobson, A., Kavan, L., Sorkine-Hornung, O.: Robust inside-outside segmentation using generalized winding numbers. ACM Trans. Graphics (TOG) 32, 1–12 (2013)

    Article  MATH  Google Scholar 

  49. Gong, K., Liang, X., Li, Y., Chen, Y., Yang, M., Lin, L.: Instance-level human parsing via part grouping network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 805–822. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_47

    Chapter  Google Scholar 

  50. Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8798–8807 (2018)

    Google Scholar 

  51. : (Axyz design 3d people models and character animation software https://secure.axyz-design.com/)

  52. Alldieck, T., Magnor, M., Xu, W., Theobalt, C., Pons-Moll, G.: Detailed human avatars from monocular video. In: 2018 International Conference on 3D Vision (3DV), pp. 98–109. IEEE (2018)

    Google Scholar 

  53. Zhang, C., Pujades, S., Black, M.J., Pons-Moll, G.: Detailed, accurate, human shape estimation from clothed 3D scan sequences. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  54. Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. The University of Texas at Dallas, Richardson, TX, USA

    Alakh Aggarwal, Jikai Wang, Steven Hogue & Xiaohu Guo

  2. Samsung Research America, Plano, TX, USA

    Saifeng Ni & Madhukar Budagavi

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  1. Alakh Aggarwal
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  2. Jikai Wang
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  3. Steven Hogue
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  4. Saifeng Ni
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  5. Madhukar Budagavi
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  6. Xiaohu Guo
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Corresponding author

Correspondence to Xiaohu Guo .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Aggarwal, A., Wang, J., Hogue, S., Ni, S., Budagavi, M., Guo, X. (2023). Layered-Garment Net: Generating Multiple Implicit Garment Layers from a Single Image. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13841. Springer, Cham. https://doi.org/10.1007/978-3-031-26319-4_23

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  • DOI: https://doi.org/10.1007/978-3-031-26319-4_23

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