{"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/latent-neural-differential-equations-for","title":"Latent Neural Differential Equations for Video Generation","arxiv_id":"2011.03864","date":"2020-11-07","proceeding":null,"authors":["Cade Gordon","Natalie Parde"],"abstract":"Generative Adversarial Networks have recently shown promise for video generation, building off of the success of image generation while also addressing a new challenge: time. Although time was analyzed in some early work, the literature has not adequately grown with temporal modeling developments. We study the effects of Neural Differential Equations to model the temporal dynamics of video generation. The paradigm of Neural Differential Equations presents many theoretical strengths including the first continuous representation of time within video generation. In order to address the effects of Neural Differential Equations, we investigate how changes in temporal models affect generated video quality. Our results give support to the usage of Neural Differential Equations as a simple replacement for older temporal generators. While keeping run times similar and decreasing parameter count, we produce a new state-of-the-art model in 64$\\times$64 pixel unconditional video generation, with an Inception Score of 15.20.","url_abs":"https://arxiv.org/abs/2011.03864v3","url_pdf":"https://arxiv.org/pdf/2011.03864v3.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":"latent-neural-differential-equations-for","repo_url":"https://github.com/Zasder3/Latent-Neural-Differential-Equations-for-Video-Generation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"unconditional-video-generation","task_name":"Unconditional Video Generation"},{"task_slug":"video-generation","task_name":"Video Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-generation-on-ucf-101-16-frames-128x128","task":"Video Generation","dataset":"UCF-101 16 frames, 128x128, Unconditional","model":"TGANv2-ODE","rank_in_archive_order":6,"of":6,"metrics":{"Inception Score":"21.02"},"uses_additional_data":false},{"leaderboard":"/sota/video-generation-on-ucf-101-16-frames-64x64","task":"Video Generation","dataset":"UCF-101 16 frames, 64x64, Unconditional","model":"TGAN-ODE","rank_in_archive_order":2,"of":7,"metrics":{"FID":"26512","Inception Score":"15.20"},"uses_additional_data":false},{"leaderboard":"/sota/video-generation-on-ucf-101-16-frames","task":"Video Generation","dataset":"UCF-101 16 frames, Unconditional, Single GPU","model":"TGANv2-ODE","rank_in_archive_order":3,"of":7,"metrics":{"Inception Score":"21.02"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2011.03864","atlas_url":"https://app.syntology.ai/?focus=2011.03864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.03864"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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