{"url":"/method/trivd-gan","slug":"trivd-gan","name":"TrIVD-GAN","full_name":"TrIVD-GAN","full_name_withheld":false,"description_markdown":"**TrIVD-GAN**, or **Transformation-based & TrIple Video Discriminator GAN**, is a type of generative adversarial network for video generation that builds upon [DVD-GAN](https://paperswithcode.com/method/dvd-gan). Improvements include a novel transformation-based recurrent unit (the TSRU) that makes the generator more expressive, and an improved discriminator architecture. \r\n\r\nIn contrast with DVD-[GAN](https://paperswithcode.com/method/gan), TrIVD-GAN has an alternative split for the roles of the discriminators, with $\\mathcal{D}\\_{S}$ judging per-frame global structure, while $\\mathcal{D}\\_{T}$ critiques local spatiotemporal structure. This is achieved by downsampling the $k$ randomly sampled frames fed to $\\mathcal{D}\\_{S}$ by a factor $s$, and cropping $T \\times H/s \\times W/s$ clips inside the high resolution video fed to $\\mathcal{D}\\_{T}$, where $T, H, W, C$ correspond to time, height, width and channel dimension of the input. This further reduces the number of pixels to process per video,\r\nfrom $k \\times H \\times W + T \\times H/s \\times W/s$ to $\\left(k + T\\right) \\times H/s \\times W/s$.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2003.04035v3","title":"Transformation-based Adversarial Video Prediction on Large-Scale Data","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Generative Models","url":"/methods/category/generative-models","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/transformation-based-adversarial-video","title":"Transformation-based Adversarial Video Prediction on Large-Scale Data","date":"2020-03-09","arxiv_id":"2003.04035","n_code_links":0,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/prediction","name":"Prediction","papers":1},{"task":"/task/video-generation","name":"Video Generation","papers":1},{"task":"/task/video-prediction","name":"Video Prediction","papers":1}],"tasks_shown":3,"n_tasks":3,"usage_by_year":[{"year":"2020","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/trivd-gan"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}