13 datasets found

A text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion.

ControlVideo: Training-free controllable text-to-video generation.

Text-to-video generation using grid diffusion models

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

The MultiStudioBench dataset contains 25 subjects, including objects, animals, etc., and there are few images for each subject. Images in the dataset are from previous works or...

WebVid dataset is used for text-to-video generation tasks.

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...

Text2video-zero: Text-to-image diffusion models are zero-shot video generators.

Videofusion: Decomposed diffusion models for high-quality video generation.

Latent-shift: Latent diffusion with temporal shift for efficient text-to-video generation.

Text-to-video is a rapidly growing research area that aims to generate a semantic, identical, and temporal coherence sequence of frames that accurately align with the input text...

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

ART•V is an efficient framework for auto-regressive video generation with diffusion models. It generates a single frame at a time, conditioned on the previous ones.