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Mesh autoencoders are commonly used for dimensionality reduction, sampling and mesh modeling.
Garland, M., Heckbert, P.S.: Surface simplification using quadric error metrics. In: ACM SIGGRAPH (1997)
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Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science (2006)
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Sorkine, O., Alexa, M.: As-rigid-as-possible surface modeling. In: Eurographics Symposium on Geometry Processing (SGP) (2007)
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Sumner, R.W., Schmid, J., Pauly, M.: Embedded deformation for shape manipulation. In: ACM SIGGRAPH (2007)
2007
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Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., Ranzuglia, G.: MeshLab: an Open-Source Mesh Processing Tool. In: Scarano, V., Chiara, R.D., Erra, U. (eds.) Eurographics Italian Chapter Conference. The Eurographics Association (2008). https://doi.org/10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2008/129-136
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Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: Computer Vision and Pattern Recognition (CVPR) (2009)
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Li, H., Adams, B., Guibas, L.J., Pauly, M.: Robust single-view geometry and motion reconstruction. In: ACM SIGGRAPH Asia (2009)
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Loper, M., Mahmood, N., Black, M.J.: Mosh: Motion and shape capture from sparse markers. ACM Transactions on Graphics (TOG) (2014)
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Zollhöfer, M., Nießner, M., Izadi, S., Rhemann, C., Zach, C., Fisher, M., Wu, C., Fitzgibbon, A., Loop, C., Theobalt, C., Stamminger, M.: Real-time non-rigid reconstruction using an rgb-d camera. ACM Transactions on Graphics (TOG) (2014)
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Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015),
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Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)
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Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: Smpl: A skinned multi-person linear model. ACM Transactions on Graphics (TOG) (2015)
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Masci, J., Boscaini, D., Bronstein, M.M., Vandergheynst, P.: Geodesic convolutional neural networks on riemannian manifolds. In: International Conference on Computer Vision Workshop (ICCVW) (2015)
2015
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Boscaini, D., Masci, J., Rodoià, E., Bronstein, M.: Learning shape correspondence with anisotropic convolutional neural networks. In: International Conference on Neural Information Processing Systems (NIPS) (2016)
2016
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Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: International Conference on Neural Information Processing Systems (NIPS) (2016)
2016
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition (CVPR) (2016)
2016
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Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: International Conference on Machine Learning (ICML) (2016)
2016
Cited alongside, same era.
Jack, D., Pontes, J.K., Sridharan, S., Fookes, C., Shirazi, S., Maire, F., Eriksson, A.: Learning free-form deformations for 3d object reconstruction. In: Asian Conference on Computer Vision (ACCV) (2018)
2018
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Kanazawa, A., Tulsiani, S., Efros, A.A., Malik, J.: Learning category-specific mesh reconstruction from image collections. In: European Conference on Computer Vision (ECCV) (2018)
2018
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Kurenkov, A., Ji, J., Garg, A., Mehta, V., Gwak, J., Choy, C., Savarese, S.: Deformnet: Free-form deformation network for 3d shape reconstruction from a single image. In: Winter Conference on Applications of Computer Vision (WACV) (2018)
2018
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Litany, O., Bronstein, A., Bronstein, M., Makadia, A.: Deformable shape completion with graph convolutional autoencoders. In: Computer Vision and Pattern Recognition (CVPR) (2018)
2018
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Bogo, F., Romero, J., Pons-Moll, G., Black, M.J.: Dynamic FAUST: Registering human bodies in motion. In: Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Cited alongside, same era.
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., Bronstein, M.M.: Geometric deep learning on graphs and manifolds using mixture model cnns. In: Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Cited alongside, same era.
Sinha, A., Unmesh, A., Huang, Q., Ramani, K.: Surfnet: Generating 3d shape surfaces using deep residual networks. In: Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Cited alongside, same era.
Yi, L., Su, H., Guo, X., Guibas, L.: Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation. In: Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Cited alongside, same era.
Bagautdinov, T., Wu, C., Saragih, J., Sheikh, Y., Fua, P.: Modeling facial geometry using compositional vaes (2018)
2018
Cited alongside, same era.
Bednařík, J., Fua, P., Salzmann, M.: Learning to reconstruct texture-less deformable surfaces. In: International Conference on 3D Vision (3DV) (2018)
2018
Cited alongside, same era.
Fuentes-Jimenez, D., Casillas-Perez, D., Pizarro, D., Collins, T., Bartoli, A.: Deep Shape-from-Template: Wide-Baseline, Dense and Fast Registration and Deformable Reconstruction from a Single Image. arXiv e-prints (2018)
2018
Cited alongside, same era.
Gao, L., Yang, J., Qiao, Y.L., Lai, Y.K., Rosin, P.L., Xu, W., Xia, S.: Automatic unpaired shape deformation transfer. ACM Transactions on Graphics (TOG) (2018)
2018
Cited alongside, same era.
Malik, J., Elhayek, A., Nunnari, F., Varanasi, K., Tamaddon, K., Héloir, A., Stricker, D.: Deephps: End-to-end estimation of 3d hand pose and shape by learning from synthetic depth. International Conference on 3D Vision (3DV) (2018)
2018
Later among the works it cites.
Pumarola, A., Agudo, A., Porzi, L., Sanfeliu, A., Lepetit, V., Moreno-Noguer, F.: Geometry-aware network for non-rigid shape prediction from a single view. In: Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Later among the works it cites.
Ranjan, A., Bolkart, T., Sanyal, S., Black, M.J.: Generating 3D faces using convolutional mesh autoencoders. In: European Conference on Computer Vision (ECCV) (2018)
2018
Later among the works it cites.
Tan, Q., Gao, L., Lai, Y.K., Xia, S.: Variational autoencoders for deforming 3d mesh models. In: Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Later among the works it cites.
Tan, Q., Gao, L., Lai, Y.K., Yang, J., Xia, S.: Mesh-based autoencoders for localized deformation component analysis. In: AAAI Conference on Artificial Intelligence (AAAI) (2018)
2018
Later among the works it cites.
Verma, N., Boyer, E., Verbeek, J.: FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis. In: Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Later among the works it cites.
Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., Jiang, Y.G.: Pixel2mesh: Generating 3d mesh models from single rgb images. In: European Conference on Computer Vision (ECCV) (2018)
2018
Later among the works it cites.
Bouritsas, G., Bokhnyak, S., Ploumpis, S., Bronstein, M., Zafeiriou, S.: Neural 3d morphable models: Spiral convolutional networks for 3d shape representation learning and generation. In: International Conference on Computer Vision (ICCV) (2019)
2019
Closest in time.
Shimada, S., Golyanik, V., Theobalt, C., Stricker, D.: Ismo-gan: Adversarial learning for monocular non-rigid 3d reconstruction. In: Computer Vision and Pattern Recognition Workshops (CVPRW) (2019)
2019
Closest in time.