{"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/generative-models-for-low-rank-video","title":"Generative Models for Low-Rank Video Representation and Reconstruction","arxiv_id":"1902.11132","date":"2019-02-25","proceeding":null,"authors":["Rakib Hyder","M. Salman Asif"],"abstract":"Finding compact representation of videos is an essential component in almost\nevery problem related to video processing or understanding. In this paper, we\npropose a generative model to learn compact latent codes that can efficiently\nrepresent and reconstruct a video sequence from its missing or under-sampled\nmeasurements. We use a generative network that is trained to map a compact code\ninto an image. We first demonstrate that if a video sequence belongs to the\nrange of the pretrained generative network, then we can recover it by\nestimating the underlying compact latent codes. Then we demonstrate that even\nif the video sequence does not belong to the range of a pretrained network, we\ncan still recover the true video sequence by jointly updating the latent codes\nand the weights of the generative network. To avoid overfitting in our model,\nwe regularize the recovery problem by imposing low-rank and similarity\nconstraints on the latent codes of the neighboring frames in the video\nsequence. We use our methods to recover a variety of videos from compressive\nmeasurements at different compression rates. We also demonstrate that we can\ngenerate missing frames in a video sequence by interpolating the latent codes\nof the observed frames in the low-dimensional space.","url_abs":"http://arxiv.org/abs/1902.11132v1","url_pdf":"http://arxiv.org/pdf/1902.11132v1.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":"generative-models-for-low-rank-video","repo_url":"https://github.com/CSIPlab/gmlr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}