VideoAuteur: Towards Long Narrative Video Generation
Recent video generation models have shown promising results in producing
high-quality video clips lasting several seconds. However, these models face
challenges in generating long sequences that convey clear and informative
events, limiting their ability to support coherent narrations. In this paper,
we present a large-scale cooking video dataset designed to advance long-form
narrative generation in the cooking domain. We validate the quality of our
proposed dataset in terms of visual fidelity and textual caption accuracy using
state-of-the-art Vision-Language Models (VLMs) and video generation models,
respectively. We further introduce a Long Narrative Video Director to enhance
both visual and semantic coherence in generated videos and emphasize the role
of aligning visual embeddings to achieve improved overall video quality. Our
method demonstrates substantial improvements in generating visually detailed
and semantically aligned keyframes, supported by finetuning techniques that
integrate text and image embeddings within the video generation process.
Project page: https://videoauteur.github.io/
678604c71f54791d52bebe30
Junfei Xiao
claimed_verified
2025-01-14T08:28:41.422Z
64b5ba6060274cbb296d6288
/avatars/67e0343954dda6e92ed3f6e7976f9f87.svg
Junfei Xiao
user
lambertxiao
678604c71f54791d52bebe31
Feng Cheng
claimed_verified
2025-04-15T08:04:04.685Z
6617eb10c1167c8d87ca818d
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/n1Mr3l8btaMM18Hao99o0.png
Feng Cheng
user
klauscc
678604c71f54791d52bebe32
Lu Qi
678604c71f54791d52bebe33
Liangke Gui
678604c71f54791d52bebe34
Jiepeng Cen
678604c71f54791d52bebe35
Zhibei Ma
claimed_verified
2025-01-16T21:55:51.675Z
645d5a44680734460f9e7742
/avatars/66687e2c413acce55436d967071f8786.svg
Zhibei Ma
user
Brightmzb
678604c71f54791d52bebe36
Alan Yuille
678604c71f54791d52bebe37
Lu Jiang
VideoAuteur: Towards Long Narrative Video Generation
Recent video generation models have shown promising results in producing
high-quality video clips lasting several seconds. However, these models face
challenges in generating long sequences that convey clear and informative
events, limiting their ability to support coherent narrations. In this paper,
we present a large-scale cooking video dataset designed to advance long-form
narrative generation in the cooking domain. We validate the quality of our
proposed dataset in terms of visual fidelity and textual caption accuracy using
state-of-the-art Vision-Language Models (VLMs) and video generation models,
respectively. We further introduce a Long Narrative Video Director to enhance
both visual and semantic coherence in generated videos and emphasize the role
of aligning visual embeddings to achieve improved overall video quality. Our
method demonstrates substantial improvements in generating visually detailed
and semantically aligned keyframes, supported by finetuning techniques that
integrate text and image embeddings within the video generation process.
Project page: https://videoauteur.github.io/
A large-scale cooking video dataset and a Long Narrative Video Director model improve visual and semantic coherence in video generation by aligning text and image embeddings.
Vision-Language Models video generation models Long Narrative Video Director visual embeddings semantic coherence keyframes finetuning techniques