{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T11:08:37Z","timestamp":1777115317424,"version":"3.51.4"},"reference-count":95,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001775","name":"University of Technology Sydney","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001775","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper studies compositional 3D-aware image synthesis for both single-object and multi-object scenes. We observe that two challenges remain in this field: existing approaches (1) lack geometry constraints and thus compromise the multi-view consistency of the single object, and (2) can not scale to multi-object scenes with complex backgrounds. To address these challenges coherently, we propose multi-view consistent generative adversarial networks (MVCGAN) for compositional 3D-aware image synthesis. First, we build the geometry constraints on the single object by leveraging the underlying 3D information. Specifically, we enforce the photometric consistency between pairs of views, encouraging the model to learn the inherent 3D shape. Second, we adapt MVCGAN to multi-object scenarios. In particular, we formulate the multi-object scene generation as a \u201cdecompose and compose\u201d process. During training, we adopt the top-down strategy to decompose training images into objects and backgrounds. When rendering, we deploy a reverse bottom-up manner by composing the generated objects and background into the holistic scene. Extensive experiments on both single-object and multi-object datasets show that the proposed method achieves competitive performance for 3D-aware image synthesis.<\/jats:p>","DOI":"10.1007\/s11263-023-01805-x","type":"journal-article","created":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T06:01:52Z","timestamp":1685253712000},"page":"2219-2242","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Multi-view Consistent Generative Adversarial Networks for Compositional 3D-Aware Image Synthesis"],"prefix":"10.1007","volume":"131","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6939-4074","authenticated-orcid":false,"given":"Xuanmeng","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhedong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daiheng","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tat-Seng","family":"Chua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,28]]},"reference":[{"key":"1805_CR1","unstructured":"(2021) Dynamic view synthesis from dynamic monocular video. In ICCV (pp. 5712\u20135721)."},{"key":"1805_CR2","unstructured":"Alhaija, HA., Mustikovela, S. K., Geiger, A., & Rother, C. (2018). Geometric image synthesis. In ACCV (pp. 85\u2013100)."},{"key":"1805_CR3","doi-asserted-by":"crossref","unstructured":"Andrew, A. M. (2001). Multiple view geometry in computer vision. Kybernetes.","DOI":"10.1108\/k.2001.30.9_10.1333.2"},{"key":"1805_CR4","doi-asserted-by":"crossref","unstructured":"Anokhin, I., Demochkin, K., Khakhulin, T., Sterkin, G., Lempitsky, V., & Korzhenkov, D. (2021) Image generators with conditionally-independent pixel synthesis. In CVPR (pp. 14278\u201314287).","DOI":"10.1109\/CVPR46437.2021.01405"},{"key":"1805_CR5","unstructured":"Brock, A., Donahue, J., & Simonyan, K. (2018). Large scale gan training for high fidelity natural image synthesis. In ICLR."},{"key":"1805_CR6","unstructured":"Burgess, C., & Kim, H. (2018). 3d shapes dataset."},{"key":"1805_CR7","doi-asserted-by":"crossref","unstructured":"Chan, E. R., Lin, C. Z., Chan, M. A., Nagano, K., Pan, B., De\u00a0Mello, S., Gallo, O., Guibas, L. J., Tremblay, J., Khamis. S, et\u00a0al. (2022). Efficient geometry-aware 3d generative adversarial networks. In CVPR (pp. 16123\u201316133).","DOI":"10.1109\/CVPR52688.2022.01565"},{"key":"1805_CR8","doi-asserted-by":"crossref","unstructured":"Chan, E. R., Monteiro, M., Kellnhofer, P., Wu, J., & Wetzstein, G. (2021). pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In CVPR (pp. 5799\u20135809).","DOI":"10.1109\/CVPR46437.2021.00574"},{"key":"1805_CR9","unstructured":"Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H. et\u00a0al. (2015). Shapenet: An information-rich 3d model repository. arXiv:1512.03012"},{"key":"1805_CR10","doi-asserted-by":"crossref","unstructured":"Chen, A., Xu, Z., Zhao, F., Zhang, X., Xiang, F., Yu, J., & Su, H. (2021). Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In ICCV (pp. 14124\u201314133).","DOI":"10.1109\/ICCV48922.2021.01386"},{"key":"1805_CR11","doi-asserted-by":"crossref","unstructured":"Chen, S. E., & Williams, L. (1993). View interpolation for image synthesis. In Conference on Computer graphics and interactive techniques.","DOI":"10.1145\/166117.166153"},{"key":"1805_CR12","doi-asserted-by":"crossref","unstructured":"Chibane, J., Bansal, A., Lazova, V., & Pons-Moll, G. (2021). Stereo radiance fields (srf): Learning view synthesis for sparse views of novel scenes. In CVPR (pp. 7911\u20137920).","DOI":"10.1109\/CVPR46437.2021.00782"},{"key":"1805_CR13","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, JW., Kim, S., & Choo, J. (2018). Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In CVPR (pp. 8789\u20138797).","DOI":"10.1109\/CVPR.2018.00916"},{"key":"1805_CR14","doi-asserted-by":"crossref","unstructured":"Choi, Y., Uh, Y., Yoo, J., & Ha, J. W. (2020). Stargan v2: Diverse image synthesis for multiple domains. In CVPR (pp. 8188\u20138197).","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"1805_CR15","doi-asserted-by":"crossref","unstructured":"Collins, R. T. (1996). A space-sweep approach to true multi-image matching. In CVPR (pp. 358\u2013363).","DOI":"10.1109\/CVPR.1996.517097"},{"key":"1805_CR16","doi-asserted-by":"crossref","unstructured":"Debevec, P. E., Taylor, C. J., & Malik, J. (1996). Modeling and rendering architecture from photographs: A hybrid geometry-and image-based approach. In Conference on Computer graphics and interactive techniques (pp. 11\u201320).","DOI":"10.1145\/237170.237191"},{"key":"1805_CR17","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). Arcface: Additive angular margin loss for deep face recognition. In CVPR (pp. 4690\u20134699).","DOI":"10.1109\/CVPR.2019.00482"},{"key":"1805_CR18","doi-asserted-by":"crossref","unstructured":"Deng, K., Liua, A., Zhu, J. Y., & Ramanan, D. (2022a). Depth-supervised nerf: Fewer views and faster training for free. In CVPR (pp. 12882\u201312891).","DOI":"10.1109\/CVPR52688.2022.01254"},{"key":"1805_CR19","doi-asserted-by":"crossref","unstructured":"Deng, Y., Yang, J., Chen, D., Wen, F., & Tong, X. (2020). Disentangled and controllable face image generation via 3d imitative-contrastive learning. In CVPR (pp. 5154\u20135163).","DOI":"10.1109\/CVPR42600.2020.00520"},{"key":"1805_CR20","doi-asserted-by":"crossref","unstructured":"Deng, Y., Yang, J., Xiang, J., & Tong, X. (2022b). Gram: Generative radiance manifolds for 3d-aware image generation. In CVPR (pp. 10673\u201310683).","DOI":"10.1109\/CVPR52688.2022.01041"},{"key":"1805_CR21","doi-asserted-by":"crossref","unstructured":"DeVries, T., Bautista, M. A., Srivastava, N., Taylor, G. W., & Susskind, J. M. (2021). Unconstrained scene generation with locally conditioned radiance fields. In ICCV (pp. 14304\u201314313).","DOI":"10.1109\/ICCV48922.2021.01404"},{"key":"1805_CR22","unstructured":"Dumoulin, V., Shlens, J., & Kudlur, M. (2020). A learned representation for artistic style."},{"issue":"4","key":"1805_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459936","volume":"40","author":"Y Feng","year":"2021","unstructured":"Feng, Y., Feng, H., Black, M. J., & Bolkart, T. (2021). Learning an animatable detailed 3d face model from in-the-wild images. ACM Transactions on Graphics (TOG), 40(4), 1\u201313.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"1805_CR24","doi-asserted-by":"crossref","unstructured":"Garbin, S. J., Kowalski, M., Johnson, M., Shotton, J., & Valentin, J. (2021). Fastnerf: High-fidelity neural rendering at 200fps. In ICCV (pp. 14346\u201314355).","DOI":"10.1109\/ICCV48922.2021.01408"},{"key":"1805_CR25","unstructured":"Girdhar, R., & Ramanan, D. (2019). Cater: A diagnostic dataset for compositional actions and temporal reasoning. In ICLR."},{"key":"1805_CR26","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac\u00a0Aodha, O., Firman, M., & Brostow, G. J. (2019). Digging into self-supervised monocular depth estimation. In ICCV.","DOI":"10.1109\/ICCV.2019.00393"},{"issue":"11","key":"1805_CR27","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial nets. Communications of the ACM, 63(11), 139\u2013144.","journal-title":"Communications of the ACM"},{"key":"1805_CR28","unstructured":"Gu, J., Liu, L., Wang, P., & Theobalt, C. (2022). Stylenerf: A style-based 3d-aware generator for high-resolution image synthesis. In ICLR."},{"key":"1805_CR29","doi-asserted-by":"crossref","unstructured":"Henderson, P., Tsiminaki, V., & Lampert, C. H. (2020). Leveraging 2d data to learn textured 3d mesh generation. In CVPR (pp. 7498\u20137507).","DOI":"10.1109\/CVPR42600.2020.00752"},{"key":"1805_CR30","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in Neural Information Processing Systems, 30"},{"key":"1805_CR31","doi-asserted-by":"crossref","unstructured":"Huang, X., & Belongie, S. (2017). Arbitrary style transfer in real-time with adaptive instance normalization. In ICCV (pp. 1501\u20131510).","DOI":"10.1109\/ICCV.2017.167"},{"key":"1805_CR32","doi-asserted-by":"crossref","unstructured":"Jeong, Y., Ahn, S., Choy, C., Anandkumar, A., Cho, M., & Park, J. (2021) Self-calibrating neural radiance fields. In ICCV (pp. 5846\u20135854).","DOI":"10.1109\/ICCV48922.2021.00579"},{"key":"1805_CR33","doi-asserted-by":"crossref","unstructured":"Johnson, J., Hariharan, B., Van Der\u00a0Maaten, L., Fei-Fei, L., Lawrence\u00a0Zitnick, C., & Girshick, R. (2017) Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In CVPR (pp. 2901\u20132910).","DOI":"10.1109\/CVPR.2017.215"},{"key":"1805_CR34","unstructured":"Kabra, R., Burgess, C., Matthey, L., Kaufman, R. L., Greff, K., Reynolds, M., & Lerchner, A. (2019). Multi-object datasets."},{"key":"1805_CR35","unstructured":"Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2018). Progressive growing of gans for improved quality, stability, and variation. In ICLR."},{"key":"1805_CR36","unstructured":"Karras, T., Aittala, M., Laine, S., H\u00e4rk\u00f6nen, E., Hellsten, J., Lehtinen, J., Aila, T. (2021). Alias-free generative adversarial networks. In NeurIPS."},{"key":"1805_CR37","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In CVPR (pp. 4401\u20134410).","DOI":"10.1109\/CVPR.2019.00453"},{"key":"1805_CR38","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020). Analyzing and improving the image quality of stylegan. In CVPR (pp. 8110\u20138119).","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"1805_CR39","unstructured":"Kingma, D. P., & Ba, J. (2015) Adam: A method for stochastic optimization. In ICLR."},{"key":"1805_CR40","unstructured":"Li, T., Slavcheva, M., Zollhoefer, M., Green, S., Lassner, C., Kim, C., Schmidt, T., Lovegrove, S., Goesele, M., & Lv, Z. (2021a). Neural 3d video synthesis. arXiv:2103.02597"},{"key":"1805_CR41","doi-asserted-by":"crossref","unstructured":"Li, Z., Niklaus, S., Snavely, N., & Wang, O. (2021b). Neural scene flow fields for space-time view synthesis of dynamic scenes. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 6498\u20136508).","DOI":"10.1109\/CVPR46437.2021.00643"},{"key":"1805_CR42","doi-asserted-by":"crossref","unstructured":"Liao, Y., Schwarz, K., Mescheder, L., & Geiger, A. (2020). Towards unsupervised learning of generative models for 3d controllable image synthesis. In CVPR (pp. 5871\u20135880).","DOI":"10.1109\/CVPR42600.2020.00591"},{"key":"1805_CR43","doi-asserted-by":"crossref","unstructured":"Lin, C. H., Ma, W. C., Torralba, A., Lucey, S. (2021). Barf: Bundle-adjusting neural radiance fields. In ICCV (pp. 5741\u20135751).","DOI":"10.1109\/ICCV48922.2021.00569"},{"key":"1805_CR44","unstructured":"Lin, C. Z., Lindell, D. B., Chan, E. R., & Wetzstein, G. (2022). 3d gan inversion for controllable portrait image animation. arXiv:2203.13441"},{"key":"1805_CR45","doi-asserted-by":"crossref","unstructured":"Lindell, D. B., Martel, J. N., & Wetzstein, G. (2021). Autoint: Automatic integration for fast neural volume rendering. In CVPR (pp. 14556\u201314565).","DOI":"10.1109\/CVPR46437.2021.01432"},{"key":"1805_CR46","doi-asserted-by":"crossref","unstructured":"Liu, Y., Peng, S., Liu, L., Wang, Q., Wang, P., Theobalt, C., Zhou, X., & Wang, W. (2022). Neural rays for occlusion-aware image-based rendering. In CVPR (pp. 7824\u20137833).","DOI":"10.1109\/CVPR52688.2022.00767"},{"issue":"4","key":"1805_CR47","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1145\/37402.37422","volume":"21","author":"WE Lorensen","year":"1987","unstructured":"Lorensen, W. E., & Cline, H. E. (1987). Marching cubes: A high resolution 3d surface construction algorithm. ACM Siggraph Computer Graphics, 21(4), 163\u2013169.","journal-title":"ACM Siggraph Computer Graphics"},{"key":"1805_CR48","doi-asserted-by":"crossref","unstructured":"Lyu, X., Liu, L., Wang, M., Kong, X., Liu, L., Liu, Y., Chen, X., & Yuan, Y. (2021). Hr-depth: High resolution self-supervised monocular depth estimation. In AAAI.","DOI":"10.1609\/aaai.v35i3.16329"},{"key":"1805_CR49","unstructured":"Matthey, L., Higgins, I., Hassabis, D., & Lerchner, A. (2017). dsprites: Disentanglement testing sprites dataset."},{"key":"1805_CR50","doi-asserted-by":"crossref","unstructured":"Meng, Q., Chen, A., Luo, H., Wu, M., Su, H., Xu, L., He, X., & Yu, J. (2021). Gnerf: Gan-based neural radiance field without posed camera. In ICCV (pp. 6351\u20136361).","DOI":"10.1109\/ICCV48922.2021.00629"},{"key":"1805_CR51","unstructured":"Mescheder, L., Geiger, A., & Nowozin, S. (2018). Which training methods for gans do actually converge? In ICML (pp. 3481\u20133490)."},{"key":"1805_CR52","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV (pp. 405\u2013421). Springer.","DOI":"10.1007\/978-3-030-58452-8_24"},{"issue":"5","key":"1805_CR53","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","volume":"31","author":"R Mur-Artal","year":"2015","unstructured":"Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015). Orb-slam: A versatile and accurate monocular slam system. IEEE Transactions on Robotics, 31(5), 1147\u20131163.","journal-title":"IEEE Transactions on Robotics"},{"key":"1805_CR54","doi-asserted-by":"crossref","unstructured":"Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., & Yang, Y. L. (2019). Hologan: Unsupervised learning of 3d representations from natural images. In CVPR (pp. 7588\u20137597).","DOI":"10.1109\/ICCV.2019.00768"},{"key":"1805_CR55","first-page":"6767","volume":"33","author":"TH Nguyen-Phuoc","year":"2020","unstructured":"Nguyen-Phuoc, T. H., Richardt, C., Mai, L., Yang, Y., & Mitra, N. (2020). Blockgan: Learning 3d object-aware scene representations from unlabelled images. Advances in Neural Information Processing Systems, 33, 6767\u20136778.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"1805_CR56","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., & Geiger, A. (2021). Giraffe: Representing scenes as compositional generative neural feature fields. In CVPR (pp. 11453\u201311464).","DOI":"10.1109\/CVPR46437.2021.01129"},{"key":"1805_CR57","doi-asserted-by":"crossref","unstructured":"Or-El R., Luo, X., Shan, M., Shechtman, E., Park, J. J., & Kemelmacher-Shlizerman, I. (2022). Stylesdf: High-resolution 3d-consistent image and geometry generation. In CVPR (pp. 13503\u201313513).","DOI":"10.1109\/CVPR52688.2022.01314"},{"key":"1805_CR58","unstructured":"Pan, X., Xu, X., Loy, C. C., Theobalt, C., & Dai, B. (2021). A shading-guided generative implicit model for shape-accurate 3d-aware image synthesis. In NeurIPS."},{"key":"1805_CR59","doi-asserted-by":"crossref","unstructured":"Peng, S., Zhang, Y., Xu, Y., Wang, Q., Shuai, Q., Bao, H., & Zhou, X. (2021). Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In CVPR (pp. 9054\u20139063).","DOI":"10.1109\/CVPR46437.2021.00894"},{"key":"1805_CR60","doi-asserted-by":"crossref","unstructured":"Pillai, S., Ambru\u015f, R., & Gaidon, A. (2019). Superdepth: Self-supervised, super-resolved monocular depth estimation. In ICRA (pp. 9250\u20139256).","DOI":"10.1109\/ICRA.2019.8793621"},{"key":"1805_CR61","doi-asserted-by":"crossref","unstructured":"Rebain, D., Jiang, W., Yazdani, S., Li, K., Yi, K. M., & Tagliasacchi, A. (2021). Derf: Decomposed radiance fields. In CVPR (pp. 14153\u201314161).","DOI":"10.1109\/CVPR46437.2021.01393"},{"key":"1805_CR62","doi-asserted-by":"crossref","unstructured":"Reiser, C., Peng, S., Liao, Y., & Geiger, A. (2021). Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 14335\u201314345).","DOI":"10.1109\/ICCV48922.2021.01407"},{"key":"1805_CR63","doi-asserted-by":"crossref","unstructured":"Schonberger, J. L., & Frahm, J. M. (2016). Structure-from-motion revisited. In CVPR (pp. 4104\u20134113).","DOI":"10.1109\/CVPR.2016.445"},{"key":"1805_CR64","first-page":"20154","volume":"33","author":"K Schwarz","year":"2020","unstructured":"Schwarz, K., Liao, Y., Niemeyer, M., & Geiger, A. (2020). Graf: Generative radiance fields for 3d-aware image synthesis. Advances in Neural Information Processing Systems, 33, 20154\u201320166.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"1805_CR65","unstructured":"Schwarz, K., Sauer, A., Niemeyer, M., Liao, Y., & Geiger, A. (2022). Voxgraf: Fast 3d-aware image synthesis with sparse voxel grids. In NeurIPS."},{"key":"1805_CR66","doi-asserted-by":"crossref","unstructured":"Seitz, S. M., & Dyer, C. R. (1996). View morphing. In Conference on computer graphics and interactive techniques (pp. 21\u201330).","DOI":"10.1145\/237170.237196"},{"key":"1805_CR67","first-page":"7462","volume":"33","author":"V Sitzmann","year":"2020","unstructured":"Sitzmann, V., Martel, J., Bergman, A., Lindell, D., & Wetzstein, G. (2020). Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33, 7462\u20137473.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"1805_CR68","unstructured":"Skorokhodov, I., Tulyakov, S., Wang, Y., & Wonka, P. (2022). Epigraf: Rethinking training of 3d gans. In NeurIPS."},{"issue":"1","key":"1805_CR69","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/A:1008192912624","volume":"32","author":"R Szeliski","year":"1999","unstructured":"Szeliski, R., & Golland, P. (1999). Stereo matching with transparency and matting. International Journal of Computer Vision, 32(1), 45\u201361.","journal-title":"International Journal of Computer Vision"},{"issue":"1","key":"1805_CR70","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/10867651.2004.10487596","volume":"9","author":"A Telea","year":"2004","unstructured":"Telea, A. (2004). An image inpainting technique based on the fast marching method. Journal of Graphics Tools, 9(1), 23\u201334.","journal-title":"Journal of Graphics Tools"},{"key":"1805_CR71","doi-asserted-by":"crossref","unstructured":"Trevithick, A., & Yang, B. (2021). Grf: Learning a general radiance field for 3d scene representation and rendering. In ICCV (pp. 15182\u201315192).","DOI":"10.1109\/ICCV48922.2021.01490"},{"key":"1805_CR72","unstructured":"Wang, Y., Tao, X., Qi, X., Shen, X., & Jia, J. (2018). Image inpainting via generative multi-column convolutional neural networks. Advances in neural information processing systems, 31."},{"issue":"4","key":"1805_CR73","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600\u2013612.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1805_CR74","unstructured":"Wang, Z., Wu, S., Xie, W., Chen, M., & Prisacariu, V. A. (2021). NeRF: Neural radiance fields without known camera parameters. arXiv:2102.07064"},{"key":"1805_CR75","doi-asserted-by":"crossref","unstructured":"Wei, Y., Liu, S., Rao, Y., Zhao, W., Lu, J., & Zhou, J. (2021). Nerfingmvs: Guided optimization of neural radiance fields for indoor multi-view stereo. In ICCV (pp. 5610\u20135619).","DOI":"10.1109\/ICCV48922.2021.00556"},{"key":"1805_CR76","unstructured":"Wu, Y., Deng, Y., Yang, J., Wei, F., Chen, Q., & Tong, X. (2022). Anifacegan: Animatable 3d-aware face image generation for video avatars. In NeurIPS."},{"key":"1805_CR77","doi-asserted-by":"crossref","unstructured":"Xian, W., Huang, J. B., Kopf, J., & Kim, C. (2021). Space-time neural irradiance fields for free-viewpoint video. In CVPR (pp. 9421\u20139431).","DOI":"10.1109\/CVPR46437.2021.00930"},{"key":"1805_CR78","unstructured":"Xiang, J., Yang, J., Deng, Y., & Tong, X. (2022). Gram-hd: 3d-consistent image generation at high resolution with generative radiance manifolds. arXiv:2206.07255"},{"key":"1805_CR79","first-page":"20683","volume":"34","author":"X Xu","year":"2021","unstructured":"Xu, X., Pan, X., Lin, D., & Dai, B. (2021). Generative occupancy fields for 3d surface-aware image synthesis. Advances in Neural Information Processing Systems, 34, 20683\u201320695.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"1805_CR80","doi-asserted-by":"crossref","unstructured":"Xu, Y., Peng, S., Yang, C., Shen, Y., & Zhou, B. (2022). 3d-aware image synthesis via learning structural and textural representations. In CVPR (pp. 18430\u201318439).","DOI":"10.1109\/CVPR52688.2022.01788"},{"key":"1805_CR81","doi-asserted-by":"crossref","unstructured":"Yao, Y., Luo, Z., Li, S., Fang, T., & Quan, L. (2018). Mvsnet: Depth inference for unstructured multi-view stereo. In ECCV (pp. 767\u2013783).","DOI":"10.1007\/978-3-030-01237-3_47"},{"key":"1805_CR82","doi-asserted-by":"crossref","unstructured":"Yen-Chen, L., Florence, P., Barron, J. T., Rodriguez, A., Isola, P., & Lin, T. Y. (2021). inerf: Inverting neural radiance fields for pose estimation. In IROS (pp. 1323\u20131330).","DOI":"10.1109\/IROS51168.2021.9636708"},{"key":"1805_CR83","doi-asserted-by":"crossref","unstructured":"Yu, A., Fridovich-Keil, S., Tancik, M., Chen, Q., Recht, B., & Kanazawa, A. (2022a). Plenoxels: Radiance fields without neural networks. In CVPR (pp. 5501\u20135510).","DOI":"10.1109\/CVPR52688.2022.00542"},{"key":"1805_CR84","doi-asserted-by":"crossref","unstructured":"Yu, A., Li, R., Tancik, M., Li, H., Ng, R., Kanazawa, A. (2021a). Plenoctrees for real-time rendering of neural radiance fields. In ICCV (pp. 5752\u20135761).","DOI":"10.1109\/ICCV48922.2021.00570"},{"key":"1805_CR85","doi-asserted-by":"crossref","unstructured":"Yu, A., Ye, V., Tancik, M., & Kanazawa, A. (2021b). pixelnerf: Neural radiance fields from one or few images. In CVPR (pp. 4578\u20134587).","DOI":"10.1109\/CVPR46437.2021.00455"},{"key":"1805_CR86","unstructured":"Yu, H. X., Guibas, L. J., Wu, J. (2022b). Unsupervised discovery of object radiance fields."},{"key":"1805_CR87","unstructured":"Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2018). mixup: Beyond empirical risk minimization. In ICLR."},{"key":"1805_CR88","unstructured":"Zhang, K., Riegler, G., Snavely, N., & Koltun, V. (2020). Nerf++: Analyzing and improving neural radiance fields. arXiv:2010.07492"},{"key":"1805_CR89","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zheng, Z., Gao, D., Zhang, B., Pan, P., & Yang, Y. (2022). Multi-view consistent generative adversarial networks for 3d-aware image synthesis. In CVPR (pp. 18450\u201318459).","DOI":"10.1109\/CVPR52688.2022.01790"},{"issue":"1","key":"1805_CR90","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","volume":"3","author":"H Zhao","year":"2016","unstructured":"Zhao, H., Gallo, O., Frosio, I., & Kautz, J. (2016). Loss functions for image restoration with neural networks. IEEE Transactions on Computational Imaging, 3(1), 47\u201357.","journal-title":"IEEE Transactions on Computational Imaging"},{"key":"1805_CR91","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Yang, X., Yu, Z., Zheng, L., Yang, Y., & Kautz, J. (2019). Joint discriminative and generative learning for person re-identification. In CVPR (pp. 2138\u20132147).","DOI":"10.1109\/CVPR.2019.00224"},{"key":"1805_CR92","unstructured":"Zhou, P., Xie, L., Ni, B., & Tian, Q. (2021). Cips-3d: A 3d-aware generator of gans based on conditionally-independent pixel synthesis. arXiv:2110.09788"},{"key":"1805_CR93","doi-asserted-by":"crossref","unstructured":"Zhou, T., Brown, M., Snavely, N., & Lowe, D. G. (2017). Unsupervised learning of depth and ego-motion from video. In CVPR (pp. 1851\u20131858).","DOI":"10.1109\/CVPR.2017.700"},{"key":"1805_CR94","doi-asserted-by":"crossref","unstructured":"Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In ICCV (pp. 2223\u20132232).","DOI":"10.1109\/ICCV.2017.244"},{"key":"1805_CR95","unstructured":"Zhu, J. Y., Zhang, Z., Zhang, C., Wu, J., Torralba, A., Tenenbaum, J., & Freeman, B. (2018). Visual object networks: Image generation with disentangled 3d representations. Advances in Neural Information Processing Systems, 31."}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01805-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-023-01805-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01805-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T07:15:17Z","timestamp":1689578117000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-023-01805-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,28]]},"references-count":95,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["1805"],"URL":"https:\/\/doi.org\/10.1007\/s11263-023-01805-x","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,28]]},"assertion":[{"value":"10 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}