Fetching the paper…
Reading the bibliography…
The success of style-based generators largely benefits from style modulation, which helps take care of the cross-instance variation within data.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
Earlier work this paper cites.
LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves, et al · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
A. Van Den Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
Earlier work this paper cites.
Generating videos with scene dynamics
C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Earlier work this paper cites.
Deformable convolutional networks
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei · 2017
Earlier work this paper cites.
Improved training of wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 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.
Arbitrary style transfer in real-time with adaptive instance normalization
X. Huang and S. Belongie · 2017
Earlier work this paper cites.
Temporal generative adversarial nets with singular value clipping
M. Saito, E. Matsumoto, and S. Saito · 2017
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.
Which training methods for GANs do actually converge?
L. Mescheder, A. Geiger, and S. Nowozin · 2018
Earlier work this paper cites.
MoCoGAN: Decomposing motion and content for video generation
S. Tulyakov, M.-Y. Liu, X. Yang, and J. Kautz · 2018
Earlier work this paper cites.
Towards accurate generative models of video: A new metric & challenges
T. Unterthiner, S. van Steenkiste, K. Kurach, R. Marinier, M. Michalski, and S. Gelly · 2018
Earlier work this paper cites.
Learning to generate time-lapse videos using multi-stage dynamic generative adversarial networks
W. Xiong, W. Luo, L. Ma, W. Liu, and J. Luo · 2018
Cited alongside, same era.
Adversarial video generation on complex datasets
A. Clark, J. Donahue, and K. Simonyan · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
Cited alongside, same era.
HoloGAN: Unsupervised learning of 3d representations from natural images
T. Nguyen-Phuoc, C. Li, L. Theis, C. Richardt, and Y.-L. Yang · 2019
Cited alongside, same era.
First order motion model for image animation
A. Siarohin, S. Lathuilière, S. Tulyakov, E. Ricci, and N. Sebe · 2019
Cited alongside, same era.
Deformable convnets v2: More deformable, better results
Long video generation with time-agnostic VQGAN and time-sensitive transformer
S. Ge, T. Hayes, H. Yang, X. Yin, G. Pang, D. Jacobs, J.-B. Huang, and D. Parikh · 2022
Later among the works it cites.
Stylenerf: A style-based 3d-aware generator for high-resolution image synthesis
J. Gu, L. Liu, P. Wang, and C. Theobalt · 2022
Later among the works it cites.
Generator knows what discriminator should learn in unconditional GANs
G. Lee, H. Kim, J. Kim, S. Kim, J.-W. Ha, and Y. Choi · 2022
Later among the works it cites.
StyleGAN-XL: Scaling StyleGAN to large diverse datasets
A. Sauer, K. Schwarz, and A. Geiger · 2022
Later among the works it cites.
3d-aware indoor scene synthesis with depth priors
Z. Shi, Y. Shen, J. Zhu, D.-Y. Yeung, and Q. Chen · 2022
Later among the works it cites.
Stylegan-V: A continuous video generator with the price, image quality and perks of StyleGAN2
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Zhu, H. Hu, S. Lin, and J. Dai · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Cited alongside, same era.
The hessian penalty: A weak prior for unsupervised disentanglement
W. Peebles, J. Peebles, J.-Y. Zhu, A. Efros, and A. Torralba · 2020
Cited alongside, same era.
Train sparsely, generate densely: Memory-efficient unsupervised training of high-resolution temporal gan
M. Saito, S. Saito, M. Koyama, and S. Kobayashi · 2020
Cited alongside, same era.
π \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.
StyleVideoGAN: A temporal generative model using a pretrained StyleGAN
G. Fox, A. Tewari, M. Elgharib, and C. Theobalt · 2021
Cited alongside, same era.
EigenGAN: Layer-wise eigen-learning for GANs
Z. He, M. Kan, and S. Shan · 2021
Cited alongside, same era.
I. Skorokhodov, S. Tulyakov, and M. Elhoseiny · 2022
Later among the works it cites.
3d-aware image synthesis via learning structural and textural representations
Y. Xu, S. Peng, C. Yang, Y. Shen, and B. Zhou · 2022
Later among the works it cites.
Improving GANs with a dynamic discriminator
C. Yang, Y. Shen, Y. Xu, D. Zhao, B. Dai, and B. Zhou · 2022
Later among the works it cites.
Generating videos with dynamics-aware implicit generative adversarial networks
S. Yu, J. Tack, S. Mo, H. Kim, J. Kim, J.-W. Ha, and J. Shin · 2022
Later among the works it cites.
Learning to drive by watching youtube videos: Action-conditioned contrastive policy pretraining
Q. Zhang, Z. Peng, and B. Zhou · 2022
Later among the works it cites.
Generative multiplane images: Making a 2d GAN 3d-aware
X. Zhao, F. Ma, D. Güera, Z. Ren, A. G. Schwing, and A. Colburn · 2022
Later among the works it cites.
GLeaD: Improving GANs with a generator-leading task
Q. Bai, C. Yang, Y. Xu, X. Liu, Y. Yang, and Y. Shen · 2023
Closest in time.
Scaling up GANs for text-to-image synthesis
M. Kang, J.-Y. Zhu, R. Zhang, J. Park, E. Shechtman, S. Paris, and T. Park · 2023
Closest in time.
The role of ImageNet classes in fr
T. Kynkäänniemi, T. Karras, M. Aittala, T. Aila, and J. Lehtinen · 2023
Closest in time.
StyleGAN-T: Unlocking the power of GANs for fast large-scale text-to-image synthesis
A. Sauer, T. Karras, S. Laine, A. Geiger, and T. Aila · 2023
Closest in time.
InternImage: Exploring large-scale vision foundation models with deformable convolutions
W. Wang, J. Dai, Z. Chen, Z. Huang, Z. Li, X. Zhu, X. Hu, T. Lu, L. Lu, H. Li, et al · 2023
Closest in time.
DisCoScene: Spatially disentangled generative radiance fields for controllable 3d-aware scene synthesis
Y. Xu, M. Chai, Z. Shi, S. Peng, I. Skorokhodov, A. Siarohin, C. Yang, Y. Shen, H.-Y. Lee, B. Zhou, et al · 2023
Closest in time.
Towards smooth video composition
Q. Zhang, C. Yang, Y. Shen, Y. Xu, and B. Zhou · 2023
Closest in time.
LinkGAN: Linking GAN latents to pixels for controllable image synthesis
J. Zhu, C. Yang, Y. Shen, Z. Shi, D. Zhao, and Q. Chen · 2023
Closest in time.