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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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.
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).
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.
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.
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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