| Analyzing and improving the image quality of stylegan T Karras, S Laine, M Aittala, J Hellsten, J Lehtinen, T Aila Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 5781 | 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, 12104-12114, 2020 | 1756 | 2020 |
| Noise2Noise: Learning image restoration without clean data J Lehtinen, J Munkberg, J Hasselgren, S Laine, T Karras, M Aittala, T Aila arXiv preprint arXiv:1803.04189, 2018 | 1713 | 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 | 1336 | 2021 |
| Elucidating the design space of diffusion-based generative models T Karras, M Aittala, T Aila, S Laine Advances in Neural Information Processing Systems 35, 26565-26577, 2022 | 804 | 2022 |
| Differentiable monte carlo ray tracing through edge sampling TM Li, M Aittala, F Durand, J Lehtinen ACM Transactions on Graphics (TOG) 37 (6), 1-11, 2018 | 492 | 2018 |
| ediff-i: Text-to-image diffusion models with an ensemble of expert denoisers Y Balaji, S Nah, X Huang, A Vahdat, J Song, Q Zhang, K Kreis, M Aittala, ... arXiv preprint arXiv:2211.01324, 2022 | 444 | 2022 |
| Single-image svbrdf capture with a rendering-aware deep network V Deschaintre, M Aittala, F Durand, G Drettakis, A Bousseau ACM Transactions on Graphics (ToG) 37 (4), 1-15, 2018 | 243 | 2018 |
| Two-shot SVBRDF capture for stationary materials. M Aittala, T Weyrich, J Lehtinen ACM Trans. Graph. 34 (4), 110:1-110:13, 2015 | 176 | 2015 |
| Reflectance modeling by neural texture synthesis M Aittala, T Aila, J Lehtinen ACM Transactions on Graphics (ToG) 35 (4), 1-13, 2016 | 158 | 2016 |
| Practical SVBRDF capture in the frequency domain. M Aittala, T Weyrich, J Lehtinen ACM Trans. Graph. 32 (4), 110:1-110:12, 2013 | 138 | 2013 |
| Burst image deblurring using permutation invariant convolutional neural networks M Aittala, F Durand Proceedings of the European conference on computer vision (ECCV), 731-747, 2018 | 132 | 2018 |
| The role of imagenet classes in fr\'echet inception distance T Kynkäänniemi, T Karras, M Aittala, T Aila, J Lehtinen arXiv preprint arXiv:2203.06026, 2022 | 127 | 2022 |
| Flexible svbrdf capture with a multi‐image deep network V Deschaintre, M Aittala, F Durand, G Drettakis, A Bousseau Computer graphics forum 38 (4), 1-13, 2019 | 107 | 2019 |
| Mixed reality for mobile construction site visualization and communication C Woodward, M Hakkarainen, O Korkalo, T Kantonen, M Aittala, K Rainio, ... Proc. 10th International Conference on Construction Applications of Virtual …, 2010 | 107 | 2010 |
| Generative novel view synthesis with 3d-aware diffusion models ER Chan, K Nagano, MA Chan, AW Bergman, JJ Park, A Levy, M Aittala, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 105 | 2023 |
| Gradient-domain metropolis light transport J Lehtinen, T Karras, S Laine, M Aittala, F Durand, T Aila ACM Transactions on Graphics (TOG) 32 (4), 1-12, 2013 | 100 | 2013 |
| Inverse lighting and photorealistic rendering for augmented reality M Aittala The visual computer 26, 669-678, 2010 | 92 | 2010 |
| Gradient-domain path tracing M Kettunen, M Manzi, M Aittala, J Lehtinen, F Durand, M Zwicker ACM Transactions on Graphics (TOG) 34 (4), 1-13, 2015 | 89 | 2015 |
| Sample-based Monte Carlo denoising using a kernel-splatting network M Gharbi, TM Li, M Aittala, J Lehtinen, F Durand ACM Transactions on Graphics (TOG) 38 (4), 1-12, 2019 | 88 | 2019 |
