Karlis Martins Briedis — accepted AI-conference papers

2026

Efficient All-Pairs Correlation Volume Sampling for Optical Flow Estimation

Karlis Martins Briedis, Markus Gross, Christopher Schroers

CVPR 2026

Recent optical flow estimation methods often employ local cost sampling from a dense all-pairs correlation volume. This results in quadratic computational and memory complexity in the number of pixels. Although an alternative memory-efficient implementation with on-demand cost computation exists, th

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2023

Frame Interpolation Transformer and Uncertainty Guidance

Markus Plack, Karlis Martins Briedis, Abdelaziz Djelouah, Matthias B. Hullin, Markus Gross, Christopher Schroers

CVPR 2023poster

Video frame interpolation has seen important progress in recent years, thanks to developments in several directions. Some works leverage better optical flow methods with improved splatting strategies or additional cues from depth, while others have investigated alternative approaches through direct…

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