paper-with-me

홈 › Papers

Phase-Based Frame Interpolation for Video

2015-06-01 · CVPR 2015 6 · Simone Meyer, Oliver Wang, Henning Zimmer, Max Grosse, Alexander Sorkine-Hornung

Standard approaches to computing interpolated (in-between) frames in a video sequence require accurate pixel correspondences between images e.g. using optical flow. We present an efficient alternative by leveraging recent developments in phase-based methods that represent motion in the phase shift of individual pixels. This concept allows in-between images to be generated by simple per-pixel phase modification, without the need for any form of explicit correspondence estimation. Up until now, such methods have been limited in the range of motion that can be interpolated, which fundamentally restricts their usefulness. In order to reduce these limitations, we introduce a novel, bounded phase shift correction method that combines phase information across the levels of a multi-scale pyramid. Additionally, we propose extensions for phase-based image synthesis that yield smoother transitions between the interpolated images. Our approach avoids expensive global optimization typical of optical flow methods, and is both simple to implement and easy to parallelize. This allows us to interpolate frames at a fraction of the computational cost of traditional optical flow-based solutions, while achieving similar quality and in some cases even superior results. Our method fails gracefully in difficult interpolation settings, e.g., significant appearance changes, where flow-based methods often introduce serious visual artifacts. Due to its efficiency, our method is especially well suited for frame interpolation and retiming of high resolution, high frame rate video.

📄 PDF Abstract BibTeX

Code (1)

owang/PhaseBasedInterpolation

Tasks

global-optimizationImage GenerationOptical Flow Estimation

Similar Papers 제목 키워드 기반

PhaseNet for Video Frame Interpolation

2018-04-03 · CVPR 2018 6 · Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung 외

Most approaches for video frame interpolation require accurate dense correspondences to synthesize an in-between frame. Therefore, they do not perform well in challenging scenarios with e.g. lighting changes or motion bl…

DecoderDeep LearningVideo Frame Interpolation

AIM 2020 Challenge on Video Temporal Super-Resolution

2020-09-28 · Sanghyun Son, Jaerin Lee, Seungjun Nah, Radu Timofte 외

Videos in the real-world contain various dynamics and motions that may look unnaturally discontinuous in time when the recordedframe rate is low. This paper reports the second AIM challenge on Video Temporal Super-Resolu…

Super-Resolution

Motion-Adjustable Neural Implicit Video Representation

2022-01-01 · CVPR 2022 1 · Long Mai, Feng Liu

Implicit neural representation (INR) has been successful in representing static images. Contemporary image-based INR, with the use of Fourier-based positional encoding, can be viewed as a mapping from sinusoidal patt…

Motion Magnification

Optimizing Video Prediction via Video Frame Interpolation

2022-06-27 · CVPR 2022 1 · Yue Wu, Qiang Wen, Qifeng Chen

Video prediction is an extrapolation task that predicts future frames given past frames, and video frame interpolation is an interpolation task that estimates intermediate frames between two frames. We have witnessed the…

Open-Ended Question AnsweringPredictionVideo Frame InterpolationVideo Prediction

AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results

2020-05-04 · Seungjun Nah, Sanghyun Son, Radu Timofte, Kyoung Mu Lee

Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reviews the first AIM challenge on video temporal super-resolution (fr…

Super-Resolution