all AI news
Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution Gap
April 23, 2024, 4:46 a.m. | Bowen Qu, Xiaoyu Liang, Shangkun Sun, Wei Gao
cs.CV updates on arXiv.org arxiv.org
Abstract: The recent advancements in Text-to-Video Artificial Intelligence Generated Content (AIGC) have been remarkable. Compared with traditional videos, the assessment of AIGC videos encounters various challenges: visual inconsistency that defy common sense, discrepancies between content and the textual prompt, and distribution gap between various generative models, etc. Target at these challenges, in this work, we categorize the assessment of AIGC video quality into three dimensions: visual harmony, video-text consistency, and domain distribution gap. For each dimension, …
abstract aigc artificial artificial intelligence arxiv assessment challenges common sense cs.cv distribution domain focus gap generated intelligence prompt quality sense text text-to-video textual type video video quality videos visual
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead Data Engineer
@ WorkMoney | New York City, United States - Remote