computer-vision-9
Image to Video Generation
3260 papers • 126 benchmarks • 313 datasets
Image to Video Generation refers to the task of generating a sequence of video frames based on a single still image or a set of still images. The goal is to produce a video that is coherent and consistent in terms of appearance, motion, and style, while also being temporally consistent, meaning that the generated video should look like a coherent sequence of frames that are temporally ordered. This task is typically tackled using deep generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), that are trained on large datasets of videos. The models learn to generate plausible video frames that are conditioned on the input image, as well as on any other auxiliary information, such as a sound or text track.
(Image credit: Papersgraph)
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Most implemented papers
Collaborative Neural Rendering using Anime Character Sheets
Zuzeng Lin, Ailin Huang, Zhewei Huang, Chen Hu, Shuchang Zhou•Mon Jul 11 2022
This paper presents the Collaborative Neural Rendering (CoNR) method, which creates new images for specified poses from a few reference images (AKA Character Sheets), and collects a character sheet dataset containing over 700,000 hand-drawn and synthesized images of diverse poses to facilitate research in this area.
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