Learning Long-Term Style-Preserving Blind Video Temporal Consistency

• URL: http://arxiv.org/abs/2103.07278v1
• Date: Fri, 12 Mar 2021 13:54:34 GMT
• Title: Learning Long-Term Style-Preserving Blind Video Temporal Consistency
• Authors: Hugo Thimonier, Julien Despois, Robin Kips, Matthieu Perrot
• Abstract summary: We propose a postprocessing model, to the transformation applied to videos, in the form of a recurrent neural network. Our model is trained using a Ping Pong procedure and its corresponding loss, recently introduced for GAN video generation. We evaluate our model on the DAVIS and videvo.net datasets and show that our approach offers state-of-the-art results concerning flicker removal.
• Score: 6.6908747077585105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When trying to independently apply image-trained algorithms to successive frames in videos, noxious flickering tends to appear. State-of-the-art post-processing techniques that aim at fostering temporal consistency, generate other temporal artifacts and visually alter the style of videos. We propose a postprocessing model, agnostic to the transformation applied to videos (e.g. style transfer, image manipulation using GANs, etc.), in the form of a recurrent neural network. Our model is trained using a Ping Pong procedure and its corresponding loss, recently introduced for GAN video generation, as well as a novel style preserving perceptual loss. The former improves long-term temporal consistency learning, while the latter fosters style preservation. We evaluate our model on the DAVIS and videvo.net datasets and show that our approach offers state-of-the-art results concerning flicker removal, and better keeps the overall style of the videos than previous approaches.

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