Abstract
This paper targets on learning-based novel view synthesis from a single or limited 2D images without the pose supervision. In the viewer-centered coordinates, we construct an end-to-end trainable conditional variational framework to disentangle the unsupervisely learned relative-pose/rotation and implicit global 3D representation (shape, texture and the origin of viewer-centered coordinates, etc.). The global appearance of the 3D object is given by several appearance-describing images taken from any number of viewpoints. Our spatial correlation module extracts a global 3D representation from the appearance-describing images in a permutation invariant manner. Our system can achieve implicitly 3D understanding without explicitly 3D reconstruction. With an unsupervisely learned viewer-centered relative-pose/rotation code, the decoder can hallucinate the novel view continuously by sampling the relative-pose in a prior distribution. In various applications, we demonstrate that our model can achieve comparable or even better results than pose/3D model-supervised learning-based novel view synthesis (NVS) methods with any number of input views.
X. Liu, T. Che and Y. Lu—Contribute Equally.
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References
Alemi, A.A., Fischer, I., Dillon, J.V., Murphy, K.: Deep variational information bottleneck. arXiv preprint arXiv:1612.00410 (2016)
Barron, J.T., Malik, J.: Shape, illumination, and reflectance from shading. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1670–1687 (2014)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 60–65. IEEE (2005)
Cao, J., Hu, Y., Yu, B., He, R., Sun, Z.: Load balanced GANs for multi-view face image synthesis. arXiv preprint arXiv:1802.07447 (2018)
Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)
Che, T., et al.: Deep verifier networks: Verification of deep discriminative models with deep generative models. arXiv preprint arXiv:1911.07421 (2019)
Chen, X., Song, J., Hilliges, O.: Monocular neural image based rendering with continuous view control. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 4090–4100 (2019)
Choy, C.B., Xu, D., Gwak, J.Y., Chen, K., Savarese, S.: 3D-R2N2: a unified approach for single and multi-view 3D object reconstruction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 628–644. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_38
Chung, F.R., Graham, F.C.: Spectral Graph Theory. No. 92. American Mathematical Soc. (1997)
Dosovitskiy, A., Springenberg, J.T., Tatarchenko, M., Brox, T.: Learning to generate chairs, tables and cars with convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 692–705 (2016)
Dosovitskiy, A., Tobias Springenberg, J., Brox, T.: Learning to generate chairs with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1538–1546 (2015)
Du, Q., Gunzburger, M., Lehoucq, R.B., Zhou, K.: Analysis and approximation of nonlocal diffusion problems with volume constraints. SIAM Rev. 54(4), 667–696 (2012)
Feng, Y., Wu, F., Shao, X., Wang, Y., Zhou, X.: Joint 3D face reconstruction and dense alignment with position map regression network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 534–551 (2018)
Flynn, J., Neulander, I., Philbin, J., Snavely, N.: DeepStereo: learning to predict new views from the world’s imagery. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5515–5524 (2016)
Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall Professional Technical Reference (2002)
Garg, R., B.G., V.K., Carneiro, G., Reid, I.: Unsupervised CNN for single view depth estimation: geometry to the rescue. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 740–756. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_45
Gilboa, G., Osher, S.: Nonlocal linear image regularization and supervised segmentation. Multisc. Model. Simul. 6(2), 595–630 (2007)
Goodfellow, I.: Nips 2016 tutorial: generative adversarial networks. arXiv preprint arXiv:1701.00160 (2016)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in neural information processing systems, pp. 2672–2680 (2014)
Han, Y., et al.: Wasserstein loss-based deep object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 998–999 (2020)
He, G., Liu, X., Fan, F., You, J.: Classification-aware semi-supervised domain adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 964–965 (2020)
He, G., Liu, X., Fan, F., You, J.: Image2Audio: facilitating semi-supervised audio emotion recognition with facial expression image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 912–913 (2020)
Henderson, P., Ferrari, V.: Learning single-image 3D reconstruction by generative modelling of shape, pose and shading. Int. J. Comput. Vis. 1–20 (2019)
Higgins, I., et al.: beta-vae: Learning basic visual concepts with a constrained variational framework. In: ICLR, vol. 2, no. 5, p. 6 (2017)
Huang, H., He, R., Sun, Z., Tan, T., et al.: IntroVAE: introspective variational autoencoders for photographic image synthesis. In: Advances in Neural Information Processing Systems, pp. 52–63 (2018)
Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1501–1510 (2017)
Insafutdinov, E., Dosovitskiy, A.: Unsupervised learning of shape and pose with differentiable point clouds. In: Advances in Neural Information Processing Systems, pp. 2802–2812 (2018)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Ji, D., Kwon, J., McFarland, M., Savarese, S.: Deep view morphing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2155–2163 (2017)
Kanazawa, A., Tulsiani, S., Efros, A.A., Malik, J.: Learning category-specific mesh reconstruction from image collections. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 371–386 (2018)
Kholgade, N., Simon, T., Efros, A., Sheikh, Y.: 3D object manipulation in a single photograph using stock 3D models. ACM Trans. Graph. (TOG) 33(4), 1–12 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kingma, D.P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., Welling, M.: Improved variational inference with inverse autoregressive flow. In: Advances in Neural Information Processing Systems, pp. 4743–4751 (2016)
Koestinger, M., Wohlhart, P., Roth, P.M., Bischof, H.: Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2144–2151. IEEE (2011)
Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. In: ICML (2016)
Lin, C.H., Kong, C., Lucey, S.: Learning efficient point cloud generation for dense 3D object reconstruction. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems, pp. 700–708 (2017)
Liu, R., et al.: An intriguing failing of convolutional neural networks and the CoordConv solution. In: Advances in Neural Information Processing Systems, pp. 9605–9616 (2018)
Liu, X.: Disentanglement for discriminative visual recognition. arXiv preprint arXiv:2006.07810 (2020)
Liu, X., B.V.K., K., Yang, C., Tang, Q., You, J.: Dependency-aware attention control for unconstrained face recognition with image sets. In: European Conference on Computer Vision (2018)
Liu, X., Fan, F., Kong, L., Diao, Z., Xie, W., Lu, J., You, J.: Unimodal regularized neuron stick-breaking for ordinal classification. Neurocomputing (2020)
Liu, X., Ge, Y., Yang, C., Jia, P.: Adaptive metric learning with deep neural networks for video-based facial expression recognition. J. Electron. Imaging 27(1), 013022 (2018)
Liu, X., Guo, Z., Jia, J., Kumar, B.: Dependency-aware attention control for imageset-based face recognition. In: IEEE Transactions on Information Forensics and Security (2019)
Liu, X., Guo, Z., Li, S., Kong, L., Jia, P., You, J., Kumar, B.V.: Permutation-invariant feature restructuring for correlation-aware image set-based recognition. In: The IEEE International Conference on Computer Vision (ICCV), October 2019
Liu, X., et al.: Importance-aware semantic segmentation in self-driving with discrete wasserstein training. In: AAAI, pp. 11629–11636 (2020)
Liu, X., Ji, W., You, J., Fakhri, G.E., Woo, J.: Severity-aware semantic segmentation with reinforced wasserstein training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12566–12575 (2020)
Liu, X., Kong, L., Diao, Z., Jia, P.: Line-scan system for continuous hand authentication. Opt. Eng. 56(3), 033106 (2017)
Liu, X., Kumar, B.V., Ge, Y., Yang, C., You, J., Jia, P.: Normalized face image generation with perceptron generative adversarial networks. In: 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA), pp. 1–8 (2018)
Liu, X., Kumar, B.V., Jia, P., You, J.: Hard negative generation for identity-disentangled facial expression recognition. Pattern Recogn. 88, 1–12 (2019)
Liu, X., Li, S., Kong, L., Xie, W., Jia, P., You, J., Kumar, B.: Feature-level Frankenstein: eliminating variations for discriminative recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 637–646 (2019)
Liu, X., Vijaya Kumar, B., You, J., Jia, P.: Adaptive deep metric learning for identity-aware facial expression recognition. In: CVPR Workshops, pp. 20–29 (2017)
Liu, X., et al.: Conservative wasserstein training for pose estimation. In: The IEEE International Conference on Computer Vision (ICCV), October 2019
Liu, X., et al.: Data augmentation via latent space interpolation for image classification. In: 24th International Conference on Pattern Recognition (ICPR), pp. 728–733 (2018)
Liu, X., Zou, Y., Song, Y., Yang, C., You, J., K Vijaya Kumar, B.: Ordinal regression with neuron stick-breaking for medical diagnosis. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)
Mathieu, M.F., Zhao, J.J., Zhao, J., Ramesh, A., Sprechmann, P., LeCun, Y.: Disentangling factors of variation in deep representation using adversarial training. In: Advances in Neural Information Processing Systems, pp. 5040–5048 (2016)
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., Yang, Y.L.: HoloGAN: unsupervised learning of 3D representations from natural images. arXiv preprint arXiv:1904.01326 (2019)
Nguyen-Phuoc, T.H., Li, C., Balaban, S., Yang, Y.: RenderNet: a deep convolutional network for differentiable rendering from 3D shapes. In: Advances in Neural Information Processing Systems, pp. 7891–7901 (2018)
Olszewski, K., Tulyakov, S., Woodford, O., Li, H., Luo, L.: Transformable bottleneck networks. arXiv preprint arXiv:1904.06458 (2019)
Park, E., Yang, J., Yumer, E., Ceylan, D., Berg, A.C.: Transformation-grounded image generation network for novel 3D view synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3500–3509 (2017)
Paszke, A., et al.: Automatic differentiation in PyTorch (2017)
Pontes, J.K., Kong, C., Sridharan, S., Lucey, S., Eriksson, A., Fookes, C.: Image2Mesh: a learning framework for single image 3D reconstruction. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11361, pp. 365–381. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20887-5_23
Rajeswar, S., Mannan, F., Golemo, F., Vazquez, D., Nowrouzezahrai, D., Courville, A.: Pix2Scene: learning implicit 3D representations from images (2018)
Rematas, K., Nguyen, C.H., Ritschel, T., Fritz, M., Tuytelaars, T.: Novel views of objects from a single image. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1576–1590 (2016)
Saxe, A.M., et al.: On the information bottleneck theory of deep learning (2018)
Shin, D., Fowlkes, C.C., Hoiem, D.: Pixels, voxels, and views: A study of shape representations for single view 3D object shape prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3061–3069 (2018)
Sturm, P., Triggs, B.: A factorization based algorithm for multi-image projective structure and motion. In: Buxton, B., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 709–720. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-61123-1_183
Sun, S.H., Huh, M., Liao, Y.H., Zhang, N., Lim, J.J.: Multi-view to novel view: synthesizing novel views with self-learned confidence. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 155–171 (2018)
Szabó, A., Favaro, P.: Unsupervised 3D shape learning from image collections in the wild. arXiv preprint arXiv:1811.10519 (2018)
Tao, Y., Sun, Q., Du, Q., Liu, W.: Nonlocal neural networks, nonlocal diffusion and nonlocal modeling. arXiv preprint arXiv:1806.00681 (2018)
Tatarchenko, M., Dosovitskiy, A., Brox, T.: Multi-view 3D models from single images with a convolutional network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 322–337. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_20
Tian, Y., Peng, X., Zhao, L., Zhang, S., Metaxas, D.N.: CR-GAN: learning complete representations for multi-view generation. arXiv preprint arXiv:1806.11191 (2018)
Tran, L., Yin, X., Liu, X.: Disentangled representation learning GAN for pose-invariant face recognition. In: CVPR, vol. 3, p. 7 (2017)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wu, J., Zhang, C., Zhang, X., Zhang, Z., Freeman, W.T., Tenenbaum, J.B.: Learning shape priors for single-view 3D completion and reconstruction. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 646–662 (2018)
Xie, J., Girshick, R., Farhadi, A.: Deep3D: fully automatic 2D-to-3D video conversion with deep convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 842–857. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_51
Xu, X., Chen, Y.C., Jia, J.: View independent generative adversarial network for novel view synthesis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 7791–7800 (2019)
Yang, C., Liu, X., Tang, Q., Kuo, C.C.J.: Towards disentangled representations for human retargeting by multi-view learning. arXiv preprint arXiv:1912.06265 (2019)
Yang, C., Song, Y., Liu, X., Tang, Q., Kuo, C.C.J.: Image inpainting using block-wise procedural training with annealed adversarial counterpart. arXiv preprint arXiv:1803.08943 (2018)
Zakharov, E., Shysheya, A., Burkov, E., Lempitsky, V.: Few-shot adversarial learning of realistic neural talking head models. arXiv preprint arXiv:1905.08233 (2019)
Zhang, X., Zhang, Z., Zhang, C., Tenenbaum, J., Freeman, B., Wu, J.: Learning to reconstruct shapes from unseen classes. In: Advances in Neural Information Processing Systems, pp. 2257–2268 (2018)
Zhou, B., Andonian, A., Torralba, A.: Temporal relational reasoning in videos. In ECCV (2018)
Zhou, T., Tulsiani, S., Sun, W., Malik, J., Efros, A.A.: View synthesis by appearance flow. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 286–301. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_18
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Zhu, X., Lei, Z., Liu, X., Shi, H., Li, S.Z.: Face alignment across large poses: a 3D solution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 146–155 (2016)
Acknowledgments
This work was supported by the Jangsu Youth Programme [grant number SBK2020041180], National Natural Science Foundation of China, Younth Programme [grant number 61705221], the Fundamental Research Funds for the Central Universities [grant number GK2240260006], NIH [NS061841, NS095986], Fanhan Technology, and Hong Kong Government General Research Fund GRF (Ref. No.152202/14E) are greatly appreciated.
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Liu, X., Che, T., Lu, Y., Yang, C., Li, S., You, J. (2020). AUTO3D: Novel View Synthesis Through Unsupervisely Learned Variational Viewpoint and Global 3D Representation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12354. Springer, Cham. https://doi.org/10.1007/978-3-030-58545-7_4
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