The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
Open accessAbstract
We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches how-ever fall short in two ways: first, they may lack an under-lying 3D representation or rely on view-inconsistent rendering, hence synthesizing images that are not multi-view consistent; second, they often depend upon representation network architectures that are not expressive enough, and their results thus lack in image quality. We propose a novel generative model, named Periodic Implicit Generative Adversarial Networks (π-GAN or pi-GAN), for high-quality 3D-aware image synthesis. π-GAN leverages neural representations with periodic activation functions and volumetric rendering to represent scenes as view-consistent radiance fields. The proposed approach obtains state-of-the-art results for 3D-aware image synthesis with multiple real and synthetic datasets.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714
Marching cubes: A high resolution 3D surface construction algorithm
Lorensen, W. E., & Cline, H. E. (1987) · Proceedings of the 14th annual conference on Computer graphics and interactive techniques · doi:10.1145/37401.37422 · Cited by 10,104
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.08500 · Cited by 8,365
Deep Learning Face Attributes in the Wild
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
Analyzing and Improving the Image Quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00813 · Cited by 6,332
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_24 · Cited by 5,351
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Tao, A., Kautz, J., & Catanzaro, B. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00917 · Cited by 4,332
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00025 · Cited by 3,422
Occupancy Networks: Learning 3D Reconstruction in Function Space
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., & Geiger, A. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00459 · Cited by 2,708
Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., & Courville, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11671 · Cited by 2,490
Learning Implicit Fields for Generative Shape Modeling
Chen, Z., & Zhang, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00609 · Cited by 1,443
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Wu, J., Zhang, C., Xue, T., Freeman, W. T., & Tenenbaum, J. B. (2016) · arXiv · doi:10.48550/arxiv.1610.07584 · Cited by 1,393 · cited 1×
“Visual Object Networks [69] and PrGANs [11] learn to synthesize 2D images by first generating a voxelized 3D shape using a 3D-GAN [64] and then projecting it into 2D.”
MethodSelf-citationPIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
Saito, S., Huang, Z., Natsume, R., Morishima, S., Li, H., & Kanazawa, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00239 · Cited by 1,228
Optical models for direct volume rendering
Max, N. (1995) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/2945.468400 · Cited by 1,059
Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision
Niemeyer, M., Mescheder, L., Oechsle, M., & Geiger, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00356 · Cited by 834
Convolutional Occupancy Networks
Peng, S., Niemeyer, M., Mescheder, L., Pollefeys, M., & Geiger, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58580-8_31 · Cited by 801
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Liu, R., Lehman, J., Molino, P., Such, F. P., Frank, E., Sergeev, A., & Yosinski, J. (2018) · arXiv · doi:10.48550/arxiv.1807.03247 · Cited by 784
Which Training Methods for GANs do actually Converge?
Mescheder, L., Geiger, A., & Nowozin, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.04406 · Cited by 768
Kajiya, J. T., & Von Herzen, B. P. (1984) · Proceedings of the 11th annual conference on Computer graphics and interactive techniques · doi:10.1145/800031.808594 · Cited by 743
Kajiya, J. T., & Von Herzen, B. P. (1984) · ACM SIGGRAPH Computer Graphics · doi:10.1145/964965.808594 · Cited by 706
Learning Category-Specific Mesh Reconstruction from Image Collections
Kanazawa, A., Tulsiani, S., Efros, A. A., & Malik, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01267-0_23 · Cited by 530
Local Implicit Grid Representations for 3D Scenes
Jiang, C., Sud, A., Makadia, A., Huang, J., NieBner, M., & Funkhouser, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00604 · Cited by 526
Neural scene representation and rendering
Eslami, S. M. A., Jimenez Rezende, D., Besse, F., Viola, F., Morcos, A. S., Garnelo, M., Ruderman, A., Rusu, A. A., Danihelka, I., Gregor, K., et al. (2018) · Science · doi:10.1126/science.aar6170 · Cited by 495
SAL: Sign Agnostic Learning of Shapes From Raw Data
Atzmon, M., & Lipman, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00264 · Cited by 433
State of the Art on Neural Rendering
Tewari, A., Fried, O., Thies, J., Sitzmann, V., Lombardi, S., Sunkavalli, K., Martin‐Brualla, R., Simon, T., Saragih, J., Nießner, M., et al. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14022 · Cited by 430
Self-citationDeep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Chabra, R., Lenssen, J. E., Ilg, E., Schmidt, T., Straub, J., Lovegrove, S., & Newcombe, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58526-6_36 · Cited by 380
Local Deep Implicit Functions for 3D Shape
Genova, K., Cole, F., Sud, A., Sarna, A., & Funkhouser, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00491 · Cited by 370
Assessing Generative Models via Precision and RecallPreprint
Sajjadi, M. S. M., Bachem, O., Lučić, M., Bousquet, O., & Gelly, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00035 · Cited by 366
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., & Yang, Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00768 · Cited by 340
Multi-view 3D Models from Single Images with a Convolutional Network
Tatarchenko, M., Dosovitskiy, A., & Brox, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46478-7_20 · Cited by 337
Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics
Niemeyer, M., Mescheder, L., Oechsle, M., & Geiger, A. (2019) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2019.00548 · Cited by 326
Learning Shape Templates With Structured Implicit Functions
Genova, K., Cole, F., Vlasic, D., Sarna, A., Freeman, W., & Funkhouser, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00725 · Cited by 323
Learning Shape Abstractions by Assembling Volumetric Primitives
Tulsiani, S., Su, H., Guibas, L. J., Efros, A. A., & Malik, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.160 · Cited by 321
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
Schwarz, K., Liao, Y., Niemeyer, M., & Geiger, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02442 · Cited by 314
3D Shape Induction from 2D Views of Multiple Objects
Gadelha, M., Maji, S., & Wang, R. (2017) · 2017 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00053 · Cited by 260
DIST: Rendering Deep Implicit Signed Distance Function With Differentiable Sphere Tracing
Liu, S., Zhang, Y., Peng, S., Shi, B., Pollefeys, M., & Cui, Z. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00209 · Cited by 256
Texture Fields: Learning Texture Representations in Function Space
Oechsle, M., Mescheder, L., Niemeyer, M., Strauss, T., & Geiger, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00463 · Cited by 252
Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the Wild
Wu, S., Rupprecht, C., & Vedaldi, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00008 · Cited by 233
Implicit Surface Representations As Layers in Neural Networks
Michalkiewicz, M., Pontes, J. K., Jack, D., Baktashmotlagh, M., & Eriksson, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00484 · Cited by 227
SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization
Jiang, Y., Ji, D., Han, Z., & Zwicker, M. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00133 · Cited by 208
Escaping Plato’s Cave: 3D Shape From Adversarial Rendering
Henzler, P., Mitra, N., & Ritschel, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01008 · Cited by 194
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., & Yang, Y.-L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00255 · Cited by 178
Dumoulin, V., Perez, E., Schucher, N., Strub, F., Vries, H., Courville, A., & Bengio, Y. (2018) · Distill · doi:10.23915/distill.00011 · Cited by 160
Cat Head Detection - How to Effectively Exploit Shape and Texture Features
Zhang, W., Sun, J. Z., & Tang, X. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88693-8_59 · Cited by 159
MetaSDF: Meta-learning Signed Distance FunctionsPreprint
Sitzmann, V., Chan, E. R., Tucker, R. P., Snavely, N., & Wetzstein, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.09662 · Cited by 145
Self-citationKellnhofer, P., Jebe, L., Jones, A., Spicer, R. P., Pulli, K., & Wetzstein, G. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00427 · Cited by 133
Self-citationBlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled ImagesPreprint
Nguyen-Phuoc, T., Richardt, C., Mai, L., Yang, Y., & Mitra, N. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.08988 · Cited by 119
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
Liao, Y., Schwarz, K., Mescheder, L., & Geiger, A. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00591 · Cited by 115
Shape and Viewpoint Without Keypoints
Goel, S., Kanazawa, A., & Malik, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_6 · Cited by 102
AmbientGAN: Generative models from lossy measurements
Bora, A., Price, E., & Dimakis, A. G. (2018) · International Conference on Learning Representations · Cited by 101
Leveraging 2D Data to Learn Textured 3D Mesh Generation
Henderson, P. D., Tsiminaki, V., & Lampert, C. H. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00752 · Cited by 97
Unsupervised Generative 3D Shape Learning from Natural ImagesPreprint
Szabó, A., Meishvili, G., & Favaro, P. (2019) · arXiv.org · doi:10.48550/arxiv.1910.00287 · Cited by 75
Implicit Mesh Reconstruction from Unannotated Image CollectionsPreprint
Tulsiani, S., Kulkarni, N. V., & Gupta, A. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.08504 · Cited by 64
Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D DataPreprint
Lunz, S., Li, Y.-Z., Fitzgibbon, A., & Kushman, N. (2020) · arXiv.org · doi:10.48550/arxiv.2002.12674 · Cited by 60
Semantic Implicit Neural Scene Representations With Semi-Supervised Training
Kohli, A., Sitzmann, V., & Wetzstein, G. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00052 · Cited by 46
Self-citationSelf-Supervised Viewpoint Learning From Image Collections
Mustikovela, S. K., Jampani, V., Mello, S. D., Liu, S., Iqbal, U., Rother, C., & Kautz, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00403 · Cited by 35
Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images Using a View-Based Representation
Rajeswar, S., Mannan, F., Golemo, F., Parent-Lévesque, J., Vázquez, D., Nowrouzezahrai, D., & Courville, A. C. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01322-1 · Cited by 9
Cited by
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
Barron, J. T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., & Srinivasan, P. P. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00580 · Cited by 1,921
RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs
Niemeyer, M., Barron, J. T., Mildenhall, B., Sajjadi, M. S. M., Geiger, A., & Radwan, N. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00540 · Cited by 609
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
Metzer, G., Richardson, E., Patashnik, O., Giryes, R., & Cohen-Or, D. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01218 · Cited by 336
StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2
Skorokhodov, I., Tulyakov, S., & Elhoseiny, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00361 · Cited by 199
Neural Articulated Radiance Field
Noguchi, A., Sun, X., Lin, S., & Harada, T. (2021) · arXiv · doi:10.48550/arxiv.2104.03110 · Cited by 177
Metadata
- Authors
- Chan, E. R., Monteiro, M., Kellnhofer, P., Wu, J., & Wetzstein, G.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 5795-5805
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3107517429
- Cited by
- 689
- References
- 64
- FWCI
- 49.64
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
