19 datasets found

Video Diffusion Models have been developed for video generation, usually integrating text and image conditioning to enhance control over the generated content.

A dataset of 30M videos along with their text captions.

The dataset used in the paper is MSR-VTT and UCF-101, two public datasets for video-text generation. MSR-VTT contains 4,900 videos with 20 manually annotated captions for each...

A comprehensive benchmark for multi-subject driven text-to-video generation, covering a wide range of subject categories and diverse subject pairs.

The Emu Video Edit model's training dataset, containing 1600 videos with 7 editing instructions each.

The dataset used for training the Emu Video Edit model, containing 1600 videos.

CogVideo is a large-scale pretrained transformer for text-to-video generation. It is trained on a dataset of 5.4 million captioned videos with a spatial resolution of 160×160.

Vidu is a high-definition text-to-video generator that demonstrates strong abilities in various aspects, including duration, coherence, and dynamism of the generated videos, on...

The dataset used in the paper for video editing tasks

The dataset used in this paper for text-to-video generation, consisting of short video clips.

The dataset used in this paper for text-to-video generation, consisting of short video clips.

The dataset used in this paper for text-to-video generation, consisting of short video clips.

The dataset used in the paper for text-to-video diffusion models

The Show-1 dataset is a text-to-video diffusion model.

The dataset used in this paper for text-to-video generation, consisting of short video clips.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used 26 text-video pairs from the public DAVIS and WebVid datasets.

WebVid-10M: A large-scale video dataset for text-to-video generation.

The authors used the pre-trained diffusion-based text-to-video (T2V) generation model without additional fine-tuning.

UCF101 dataset is used to test the proposed text-to-video model. The dataset contains 101 action categories, and each category has 10 videos. The videos are labeled with text...