{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/improving-video-generation-for-multi","title":"Improving Video Generation for Multi-functional Applications","arxiv_id":"1711.11453","date":"2017-11-30","proceeding":null,"authors":["Bernhard Kratzwald","Zhiwu Huang","Danda Pani Paudel","Acharya Dinesh","Luc van Gool"],"abstract":"In this paper, we aim to improve the state-of-the-art video generative\nadversarial networks (GANs) with a view towards multi-functional applications.\nOur improved video GAN model does not separate foreground from background nor\ndynamic from static patterns, but learns to generate the entire video clip\nconjointly. Our model can thus be trained to generate - and learn from - a\nbroad set of videos with no restriction. This is achieved by designing a robust\none-stream video generation architecture with an extension of the\nstate-of-the-art Wasserstein GAN framework that allows for better convergence.\nThe experimental results show that our improved video GAN model outperforms\nstate-of-theart video generative models on multiple challenging datasets.\nFurthermore, we demonstrate the superiority of our model by successfully\nextending it to three challenging problems: video colorization, video\ninpainting, and future prediction. To the best of our knowledge, this is the\nfirst work using GANs to colorize and inpaint video clips.","url_abs":"http://arxiv.org/abs/1711.11453v2","url_pdf":"http://arxiv.org/pdf/1711.11453v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"improving-video-generation-for-multi","repo_url":"https://github.com/bernhard2202/improved-video-gan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"colorization","task_name":"Colorization"},{"task_slug":"future-prediction","task_name":"Future prediction"},{"task_slug":"video-generation","task_name":"Video Generation"},{"task_slug":"video-inpainting","task_name":"Video Inpainting"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.11453","atlas_url":"https://app.syntology.ai/?focus=1711.11453","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}