arXiv:2606. 01285v1 Announce Type: cross Abstract: Text-to-video generation has advanced rapidly in visual quality, but remains under-evaluated for factuality and practical usefulness.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
AI video generation has advanced rapidly and entered widespread commercial use. As a result, quality differences among videos produced by state-of-the-art AI video generation models~(AIVGMs) have become increasingly difficult to discern using conventional evaluation criteria, such as visual fidelity and semantic instruction following.
arXiv:2608. 09111v1 Announce Type: new Abstract: AI video generation has advanced rapidly and entered widespread commercial use.
arXiv:2602. 13602v2 Announce Type: replace-cross Abstract: We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA).
arXiv:2606. 09064v1 Announce Type: cross Abstract: Recent advances in Video Large Language Models (Video-LLMs) have enabled performance on long-video understanding tasks.
Video generation models are increasingly capable of producing realistic videos, but they still struggle to generate videos that follow basic physical laws. Compounding this is a lack of reliable granular evaluation methods for localizing and specifying physical law violations in videos.
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.