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Simon Niklaus - Staff Research Scientist at Google DeepMind
Project Indigo 06.2025 Reviewer Awards 06.2025 Video Denoising 04.2025 Clean Machine 10.2024 Image Denoising 10.2024 3D Mockup 10.2024 Sky Segmentation 10.2024 Firefly Video 09.2024 Intern Alumni 09.2024 Lens Blur 05.2024 Structure Reference 04.2024 Interpolation Benchmark 03.2024 Adobe Firefly 03.2023 Splatting Synthesis 01.2022 arXiv DOOM 10.2021 Project Inbetween 10.2021 Dark Papers 11.2020 Revisited Convolution 10.2020 Dual Dereflection 10.2020 NES Memoryview 03.2020 Softmax Splatting 03.2020 3D Ken Burns 09.2019 Moving Stills 10.2018 Context Synthesis 03.2018 Visual Computing 01.2018 WASM Raytracer 11.2017 Separable Convolution 08.2017 Web Development 06.2017 Adaptive Convolution 03.2017 Rotating Pipes 02.2017 Rollercoaster Map 06.2016 Connect Four 05.2016 Mini Chess 05.2016 Artificial Intelligence 03.2016 Youtube Watchmarker 03.2015 Terms and Conditions Privacy Policy
Simon Niklaus, Ph.D.
- Staff Research Scientist at Google DeepMind
- Working with Oliver Wang
- Formerly with Eli Shechtman
- Alumni of Feng Liu
- Living in Vancouver, WA
- Here is my resume
- linkedin.com/in/sniklaus
- github.com/sniklaus
- bsky.app/profile/sniklaus.com
![]() | Benchmarking Video Frame Interpolation Simon Kiefhaber, Simon Niklaus, Feng Liu, and Simone Schaub-Meyer Frame interpolation is becoming a popular research target. However, the current evaluation of interpolation techniques is not ideal. We strongly believe that the community would greatly benefit from a benchmark, which is what we propose. |
![]() | Revisiting Adaptive Convolutions for Video Frame Interpolation Simon Niklaus, Long Mai, and Oliver Wang We show, somewhat surprisingly, that it is possible to achieve near state-of-the-art frame interpolation results with an older and simpler approach, namely adaptive separable convolutions, through a subtle set of low level improvements. |
![]() | Learned Dual-View Reflection Removal S. Niklaus, X. Zhang, J. T. Barron, N. Wadhwa, R. Garg, F. Liu, and T. Xue Typical dereflection algorithms either use a single input image, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient. We instead propose an algorithm that uses stereo images as input. |


































