computer-vision-10

Video Temporal Consistency

3260 papers • 126 benchmarks • 313 datasets

A method that remove temporal flickering and other artifacts from videos, in particular those introduced by (non-temporal-aware) per-frame processing

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ChenyangLEI/deep-video-prior

2 papers 319

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Most implemented papers

Blind Video Temporal Consistency via Deep Video Prior

Qifeng Chen, Chenyang Lei, Yazhou Xing•Wed Oct 21 2020

This work shows that temporal consistency can be achieved by training a convolutional network on a video with the Deep Video Prior, and proposes a carefully designed iteratively reweighted training strategy to address the challenging multimodal inconsistency problem.

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Learning Blind Video Temporal Consistency

Eli Shechtman, Oliver Wang, Ming-Hsuan Yang, Wei-Sheng Lai, Ersin Yumer, Jia-Bin Huang•Tue Jul 31 2018

An efficient approach based on a deep recurrent network for enforcing temporal consistency in a video that can handle multiple and unseen tasks, including but not limited to artistic style transfer, enhancement, colorization, image-to-image translation and intrinsic image decomposition.

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Interactive Control over Temporal Consistency while Stylizing Video Streams

Sumit Shekhar, M. Reimann, Moritz Hilscher, Amir Semmo, J. Dollner, Matthias Trapp•Sun Jan 01 2023

This work proposes an approach that stylizes video streams in real‐time at full HD resolutions while providing interactive consistency control and develops a lite optical‐flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy.

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Deep Video Prior for Video Consistency and Propagation

Qifeng Chen, Hao Ouyang, Chenyang Lei, Yazhou Xing•Mon Jan 10 2022

This work shows that temporal consistency can be achieved by training a convolutional network on a video with Deep Video Prior (DVP), and shows its effectiveness in propagating three different types of information (color, artistic style, and object segmentation).

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Blind Video Deflickering by Neural Filtering with a Flawed Atlas

Zhaoxiang Zhang, Qifeng Chen, Chenyang Lei, Xuanchi Ren•Mon Mar 13 2023

This work proposes a general flicker removal framework that only receives a single flickering video as input without additional guidance, and achieves satisfying deflickering performance and even outperforms baselines that use extra guidance on a public benchmark.

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