Invideo AI uses OpenAI’s GPT-4. 1, gpt-image-1, and text-to-speech models to transform creative ideas into professional videos in minutes.
arXiv:2608. 13210v1 Announce Type: cross Abstract: Long-form video understanding encompasses tasks that go beyond retrieving isolated events, including tracking an evolving narrative and interpreting social meaning that may remain implicit.
By Yuheng Huang, Jianlang Chen, Jiayang Song, Hua Qi, Aza Kai, Vincent Markert, Edison Marrese-Taylor, Jianjun Zhao, Lei Ma
arXiv:2606. 12300v1 Announce Type: cross Abstract: Temporal grounding--returning the interval $[t_s, t_e]$ for a natural-language query over a video--is the language interface to long-form video, yet has been studied on short videos; the dynamics of hour-scale natural-language grounding remain underexplored.
By Sukmin Seo, Geewook Kim
arXiv:2604. 25220v2 Announce Type: replace Abstract: Data videos combine animated visualizations with synchronized narration to communicate quantitative information and are widely used in journalism, education, and public communication.
By Ridwan Mahbub, Syem Aziz, Mizanur Rahman, Mahir Ahmed, Shadikur Rahman, Shafiq Joty, Enamul Hoque
arXiv:2607. 01086v1 Announce Type: cross Abstract: The evaluation of long-term video quality understanding remains an open challenge for large vision-language models (LVLMs).
By Arpita Nema, Hanwei Zhu, Xi Zhang, Weisi Lin
Understanding dynamic sound sources requires jointly determining what produces a sound, where the source is located, and how it moves over time. Yet existing audio-language models often represent clips as global acoustic events, while vision-language models lack the spatial audio cues needed to localize and track individual sources.