| 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 | 4902 | 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 | 2107 | 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 | 879 | 2018 |
| 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 | 550 | 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 | 434 | 2019 |
| GANSpace: Discovering Interpretable GAN Controls E Härkönen, A Hertzmann, J Lehtinen, S Paris Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020 | 328 | 2020 |
| 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 | 289 | 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 | 222 | 2017 |
| 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 | 209 | 2021 |
| 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 | 203 | 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 | 199 | 2019 |
| 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 | 175 | 2007 |
| 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 | 167 | 2015 |
| 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 | 164 | 2019 |
| Two-shot SVBRDF capture for stationary materials M Aittala, T Weyrich, J Lehtinen ACM Transactions on Graphics 34 (4), 110, 2015 | 133 | 2015 |
| Matrix radiance transfer J Lehtinen, J Kautz Proceedings of the 2003 symposium on Interactive 3D graphics, 59-64, 2003 | 126 | 2003 |
| Reflectance modeling by neural texture synthesis M Aittala, T Aila, J Lehtinen ACM Transactions on Graphics (ToG) 35 (4), 1-13, 2016 | 116 | 2016 |
| Temporal light field reconstruction for rendering distribution effects J Lehtinen, T Aila, J Chen, S Laine, F Durand ACM SIGGRAPH 2011 papers, 1-12, 2011 | 113 | 2011 |
| Decoupled sampling for graphics pipelines J Ragan-Kelley, J Lehtinen, J Chen, M Doggett, F Durand ACM Transactions on Graphics (TOG) 30 (3), 1-17, 2011 | 112 | 2011 |
| Production-level facial performance capture using deep convolutional neural networks S Laine, T Karras, T Aila, A Herva, S Saito, R Yu, H Li, J Lehtinen Proceedings of the ACM SIGGRAPH/Eurographics symposium on computer animation …, 2017 | 108* | 2017 |
Timo AilaSenior Distinguished Research Scientist, NVIDIAVerified email at nvidia.com
