Fetching the paper…
Reading the bibliography…
State-of-the-art 3D-aware generative models rely on coordinate-based MLPs to parameterize 3D radiance fields.
Generative adversarial nets
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
CARLA: An open urban driving simulator
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun · 2017
Earlier work this paper cites.
Weakly supervised generative adversarial networks for 3d reconstruction
J. Gwak, C. B. Choy, A. Garg, M. Chandraker, and S. Savarese · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
Earlier work this paper cites.
Demystifying MMD gans
M. Binkowski, D. J. Sutherland, M. Arbel, and A. Gretton · 2018
Earlier work this paper cites.
Progressive growing of GANs for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2018
Earlier work this paper cites.
Cat hipsterizer, 2018
T. B. Lee · 2018
Earlier work this paper cites.
Which training methods for gans do actually converge?
L. Mescheder, A. Geiger, and S. Nowozin · 2018
Earlier work this paper cites.
Visual object networks: Image generation with disentangled 3D representations
J.-Y. Zhu, Z. Zhang, C. Zhang, J. Wu, A. Torralba, J. B. Tenenbaum, and W. T. Freeman · 2018
Earlier work this paper cites.
Large scale GAN training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2019
Earlier work this paper cites.
Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set
Y. Deng, J. Yang, S. Xu, D. Chen, Y. Jia, and X. Tong · 2019
Earlier work this paper cites.
Escaping plato’s cave: 3d shape from adversarial rendering
P. Henzler, N. J. Mitra, , and T. Ritschel · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
Earlier work this paper cites.
Hologan: Unsupervised learning of 3d representations from natural images
T. Nguyen-Phuoc, C. Li, L. Theis, C. Richardt, and Y.-L. Yang · 2019
Earlier work this paper cites.
Texture fields: Learning texture representations in function space
M. Oechsle, L. Mescheder, M. Niemeyer, T. Strauss, and A. Geiger · 2019
Earlier work this paper cites.
Stargan v2: Diverse image synthesis for multiple domains
Y. Choi, Y. Uh, J. Yoo, and J.-W. Ha · 2020
Earlier work this paper cites.
Generative sparse detection networks for 3d single-shot object detection
J. Gwak, C. B. Choy, and S. Savarese · 2020
Earlier work this paper cites.
Ganspace: Discovering interpretable GAN controls
E. Härkönen, A. Hertzmann, J. Lehtinen, and S. Paris · 2020
Cited alongside, same era.
Training generative adversarial networks with limited data
T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila · 2020
Cited alongside, same era.
Analyzing and improving the image quality of StyleGAN
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila · 2020
Cited alongside, same era.
Towards unsupervised learning of generative models for 3d controllable image synthesis
Y. Liao, K. Schwarz, L. Mescheder, and A. Geiger · 2020
Cited alongside, same era.
Neural sparse voxel fields
L. Liu, J. Gu, K. Z. Lin, T. Chua, and C. Theobalt · 2020
Cited alongside, same era.
NeRF: Representing scenes as neural radiance fields for view synthesis
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng · 2020
Lifting 2d stylegan for 3d-aware face generation
Y. Shi, D. Aggarwal, and A. K. Jain · 2021
Later among the works it cites.
Neural geometric level of detail: Real-time rendering with implicit 3D shapes
T. Takikawa, J. Litalien, K. Yin, K. Kreis, C. Loop, D. Nowrouzezahrai, A. Jacobson, M. McGuire, and S. Fidler · 2021
Later among the works it cites.
Generative occupancy fields for 3d surface-aware image synthesis
X. Xu, X. Pan, D. Lin, and B. Dai · 2021
Later among the works it cites.
PlenOctrees for real-time rendering of neural radiance fields
A. Yu, R. Li, M. Tancik, H. Li, R. Ng, and A. Kanazawa · 2021
Later among the works it cites.
3d-aware semantic-guided generative model for human synthesis
J. Zhang, E. Sangineto, H. Tang, A. Siarohin, Z. Zhong, N. Sebe, and W. Wang · 2021
Later among the works it cites.
Image gans meet differentiable rendering for inverse graphics and interpretable 3d neural rendering
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger · 2020
Cited alongside, same era.
GRAF: generative radiance fields for 3d-aware image synthesis
K. Schwarz, Y. Liao, M. Niemeyer, and A. Geiger · 2020
Cited alongside, same era.
Graf: Generative radiance fields for 3d-aware image synthesis
K. Schwarz, Y. Liao, M. Niemeyer, and A. Geiger · 2020
Cited alongside, same era.
Fourier features let networks learn high frequency functions in low dimensional domains
M. Tancik, P. Srinivasan, B. Mildenhall, S. Fridovich-Keil, N. Raghavan, U. Singhal, R. Ramamoorthi, J. Barron, and R. Ng · 2020
Cited alongside, same era.
Pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis
E. R. Chan, M. Monteiro, P. Kellnhofer, J. Wu, and G. Wetzstein · 2021
Cited alongside, same era.
Cg-nerf: Conditional generative neural radiance fields
K. Jo, G. Shim, S. Jung, S. Yang, and J. Choo · 2021
Cited alongside, same era.
Y. Zhang, W. Chen, H. Ling, J. Gao, Y. Zhang, A. Torralba, and S. Fidler · 2021
Later among the works it cites.
CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis
P. Zhou, L. Xie, B. Ni, and Q. Tian · 2021
Later among the works it cites.
Plenoxels: Radiance fields without neural networks
Alex Yu and Sara Fridovich-Keil, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa · 2022
Closest in time.
Efficient geometry-aware 3D generative adversarial networks
E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. D. Mello, O. Gallo, L. Guibas, J. Tremblay, S. Khamis, T. Karras, and G. Wetzstein · 2022
Closest in time.
Gram: Generative radiance manifolds for 3d-aware image generation
Y. Deng, J. Yang, J. Xiang, and X. Tong · 2022
Closest in time.
Stylenerf: A style-based 3d-aware generator for high-resolution image synthesis
J. Gu, L. Liu, P. Wang, and C. Theobalt · 2022
Closest in time.
Zero-shot text-guided object generation with dream fields
A. Jain, B. Mildenhall, J. T. Barron, P. Abbeel, and B. Poole · 2022
Closest in time.
Instant neural graphics primitives with a multiresolution hash encoding
T. Müller, A. Evans, C. Schied, and A. Keller · 2022
Closest in time.
Stylesdf: High-resolution 3d-consistent image and geometry generation
R. Or-El, X. Luo, M. Shan, E. Shechtman, J. Park, and I. Kemelmacher · 2022
Closest in time.
Stylegan-xl: Scaling stylegan to large diverse datasets
A. Sauer, K. Schwarz, and A. Geiger · 2022
Closest in time.
Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction
C. Sun, M. Sun, and H.-T. Chen · 2022
Closest in time.
3d-aware image synthesis via learning structural and textural representations
Y. Xu, S. Peng, C. Yang, Y. Shen, and B. Zhou · 2022
Closest in time.
GIRAFFE HD: A high-resolution 3d-aware generative model
Y. Xue, Y. Li, K. K. Singh, and Y. J. Lee · 2022
Closest in time.