| Progressive growing of GANs for improved quality, stability, and variation T Karras, T Aila, S Laine, J Lehtinen International Conference on Learning Representations (ICLR), 2018 | 8662 | 2018 |
| Analyzing and improving the image quality of StyleGAN T Karras, S Laine, M Aittala, J Hellsten, J Lehtinen, T Aila Proc. Computer Vision and Pattern Recognition (CVPR), 2020 | 6595 | 2020 |
| Training generative adversarial networks with limited data T Karras, M Aittala, J Hellsten, S Laine, J Lehtinen, T Aila Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020 | 1986 | 2020 |
| Noise2Noise: Learning image restoration without clean data J Lehtinen, J Munkberg, J Hasselgren, S Laine, T Karras, M Aittala, T Aila International Conference on Machine Learning (ICML), 2018 | 1897 | 2018 |
| Alias-free generative adversarial networks T Karras, M Aittala, S Laine, E Härkönen, J Hellsten, J Lehtinen, T Aila Advances in neural information processing systems 34, 852-863, 2021 | 1569 | 2021 |
| GANSpace: Discovering Interpretable GAN Controls E Härkönen, A Hertzmann, J Lehtinen, S Paris Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020 | 943 | 2020 |
| Few-shot unsupervised image-to-image translation MY Liu, X Huang, A Mallya, T Karras, T Aila, J Lehtinen, J Kautz International Conference on Computer Vision (ICCV) 2019, 2019 | 871 | 2019 |
| Improved precision and recall metric for assessing generative models T Kynkäänniemi, T Karras, S Laine, J Lehtinen, T Aila Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019 | 730 | 2019 |
| Differentiable Monte Carlo Ray Tracing through Edge Sampling TM Li, M Aittala, F Durand, J Lehtinen ACM Transactions on Graphics (Proc. SIGGRAPH Asia 2018) 37 (6), Article 222, 2018 | 537 | 2018 |
| Audio-driven facial animation by joint end-to-end learning of pose and emotion T Karras, T Aila, S Laine, A Herva, J Lehtinen ACM Transactions on Graphics (ToG) 36 (4), 1-12, 2017 | 471 | 2017 |
| Progressive growing of gans for improved quality, stability, and variation. arXiv 2017 T Karras, T Aila, S Laine, J Lehtinen arXiv preprint arXiv:1710.10196, 1-26, 2018 | 458 | 2018 |
| Learning to predict 3D objects with an interpolation-based differentiable renderer W Chen, H Ling, J Gao, E Smith, J Lehtinen, A Jacobson, S Fidler Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 9605-9616, 2019 | 418 | 2019 |
| Modular primitives for high-performance differentiable rendering S Laine, J Hellsten, T Karras, Y Seol, J Lehtinen, T Aila ACM Transactions on Graphics (ToG) 39 (6), 1-14, 2020 | 372 | 2020 |
| High-quality self-supervised deep image denoising S Laine, T Karras, J Lehtinen, T Aila Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019 | 366 | 2019 |
| Recent advances in adaptive sampling and reconstruction for Monte Carlo rendering M Zwicker, W Jarosz, J Lehtinen, B Moon, R Ramamoorthi, F Rousselle, ... Computer Graphics Forum 34 (2), 667-681, 2015 | 220 | 2015 |
| Two-shot SVBRDF capture for stationary materials M Aittala, T Weyrich, J Lehtinen ACM Transactions on Graphics 34 (4), 110, 2015 | 183 | 2015 |
| Incremental instant radiosity for real-time indirect illumination S Laine, H Saransaari, J Lehtinen, J Kontkanen, T Aila Proc. Eurographics Symposium on Rendering 2007, 4-8, 2007 | 181 | 2007 |
| Reflectance modeling by neural texture synthesis M Aittala, T Aila, J Lehtinen ACM Transactions on Graphics (ToG) 35 (4), 1-13, 2016 | 169 | 2016 |
| The Role of ImageNet Classes in Fréchet Inception Distance T Kynkäänniemi, T Karras, M Aittala, T Aila, J Lehtinen International Conference on Learning Representations (ICLR), 2022 | 166 | 2022 |
| Practical SVBRDF capture in the frequency domain. M Aittala, T Weyrich, J Lehtinen ACM Trans. Graph. 32 (4), 110:1-110:12, 2013 | 145 | 2013 |
Timo AilaSenior Distinguished Research Scientist, NVIDIAVerified email at nvidia.com
