Abstract
In this paper, we propose 3D Context-Aware PIFu to recover 3D clothed human from a single image. Existing implicit function-based models suffer from the unsatisfied robustness to poor pose variations, since they ignore the inherent geometric relationship among 3D points. In this work, we utilize the 3D human model as a strong prior to regularize the reconstruction. With a fitted 3D human model, a global shape is extracted by the Pointnet to handle pose variations, and a local feature is extracted by Graph Convolutional Neural Network (GCNN) to capture geometry details. Besides, to enable the reconstruction network to capture fine-grained geometry on 3D cloth, we propose a multi-view implicit differentiable loss to directly measure the visual effect. Experimental results show that our approach is more robust to pose variations and reconstructs the human body with more details.
T. Liao and X. Zhu—Equal contributions.
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References
Chibane, J., Alldieck, T., Pons-Moll, G.: Implicit functions in feature space for 3D shape reconstruction and completion. In: 2020 CVPR, pp. 6968–6979 (2020)
He, T., Collomosse, J., Jin, H., Soatto, S.: Geo-PIFu: geometry and pixel aligned implicit functions for single-view human reconstruction. In: Conference on Neural Information Processing Systems (NIPS). (2020)
Huang, Z., Xu, Y., Lassner, C., Li, H., Tung, T.: ARCH: animatable reconstruction of clothed humans. In: 2020 CVPR, pp. 3090–3099 (2020)
Saito, S., Huang, Z., Natsume, R., Morishima, S., Li, H., Kanazawa, A.: PIFu: pixel-aligned implicit function for high-resolution clothed human digitization. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2304–2314 (2019)
Saito, S., Simon, T., Saragih, J., Joo, H.: PIFuHD: Multi-level pixel-aligned implicit function for high-resolution 3D human digitization. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 81–90 (2020)
Zheng, Z., Yu, T., Wei, Y., Dai, Q., Liu, Y.: Deephuman: 3D human reconstruction from a single image. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 7738–7748 (2019)
Charles, R.Q., Su, H., Kaichun, M., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 77–85 (2017)
Kolotouros, N., Pavlakos, G., Black, M., Daniilidis, K.: Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2252–2261 (2019)
Güler, R.A., Kokkinos, I.: HoloPose: holistic 3D human reconstruction in-the-wild. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10876–10886 (2019)
Lähner, Z., Cremers, D., Tung, T.: DeepWrinkles: accurate and realistic clothing modeling. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 698–715. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_41
Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7122–7131 (2018)
Alldieck, T., Magnor, M., Xu, W., Theobalt, C., Pons-Moll, G.: Video based reconstruction of 3D people models. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8387–8397 (2018)
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. (TOG) 34(6), 1–16 (2015)
Bhatnagar, B., Tiwari, G., Theobalt, C., Pons-Moll, G.: Multi-garment net: learning to dress 3D people from images. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5419–5429 (2019)
Ma, Q., et al.: Learning to dress 3D people in generative clothing. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6468–6477 (2020)
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
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
Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 165–174 (2019)
Liu, S., Zhang, Y., Peng, S., Shi, B., Pollefeys, M., Cui, Z.: Dist: rendering deep implicit signed distance function with differentiable sphere tracing. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2016–2025 (2020)
Natsume, R., et al.: SiCloPe: silhouette-based clothed people. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4480–4490 (2019)
Loper, M.M., Black, M.J.: OpenDR: an approximate differentiable renderer. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 154–169. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10584-0_11
Liu, S., Chen, W., Li, T., Li, H.: Soft rasterizer: a differentiable renderer for image-based 3D reasoning. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 7707–7716 (2019)
Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: learning implicit 3D representations without 3D supervision. In: CVPR, pp. 3501–3512 (2020)
Insafutdinov, E., Dosovitskiy, A.: Unsupervised learning of shape and pose with differentiable point clouds. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R., (eds.) Conference on Neural Information Processing Systems (NIPS). Volume 31, Curran Associates, Inc., (2018)
Liu, S., Saito, S., Chen, W., Li, H.: Learning to infer implicit surfaces without 3D supervision. In: Advances in Neural Information Processing Systems(NIPS), pp. 8295–8306 (2019)
Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. ACM SIGGRAPH Comput. Graph. 21(4), 163–169 (1987)
Zhang, C., Pujades, S., Black, M., Pons-Moll, G.: Detailed, accurate, human shape estimation from clothed 3D scan sequences. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5484–5493 (2017)
Acknowledgements
This work was supported in part by the National Key Research & Development Program (No. 2020AAA0140002), Chinese National Natural Science Foundation Projects #61806196, #61876178, #61976229, #61872367.
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Liao, T., Zhu, X., Lei, Z., Li, S.Z. (2021). 3D Context-Aware PIFu for Clothed Human Reconstruction. In: Feng, J., Zhang, J., Liu, M., Fang, Y. (eds) Biometric Recognition. CCBR 2021. Lecture Notes in Computer Science(), vol 12878. Springer, Cham. https://doi.org/10.1007/978-3-030-86608-2_15
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