{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T23:29:01Z","timestamp":1785022141598,"version":"3.55.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Developing conditional generative models for text-to-video synthesis is an extremely challenging yet an important topic of research in machine learning. In this work, we address this problem by introducing Text-Filter conditioning Generative Adversarial Network (TFGAN), a conditional GAN model with a novel multi-scale text-conditioning scheme that improves text-video associations. By combining the proposed conditioning scheme with a deep GAN architecture, TFGAN generates high quality videos from text on challenging real-world video datasets. In addition, we construct a synthetic dataset of text-conditioned moving shapes to systematically evaluate our conditioning scheme. Extensive experiments demonstrate that TFGAN significantly outperforms existing approaches, and can also generate videos of novel categories not seen during training.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/276","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"1995-2001","source":"Crossref","is-referenced-by-count":96,"title":["Conditional GAN with Discriminative Filter Generation for Text-to-Video Synthesis"],"prefix":"10.24963","author":[{"given":"Yogesh","family":"Balaji","sequence":"first","affiliation":[{"name":"University of Maryland, College Park"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin Renqiang","family":"Min","sequence":"additional","affiliation":[{"name":"NEC Labs America - Princeton"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Bai","sequence":"additional","affiliation":[{"name":"NEC Labs America - Princeton"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rama","family":"Chellappa","sequence":"additional","affiliation":[{"name":"University of Maryland, College Park"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hans Peter","family":"Graf","sequence":"additional","affiliation":[{"name":"NEC Labs America - Princeton"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:48:03Z","timestamp":1564285683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/276"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/276","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}