Video Generation
241 papers with code • 15 benchmarks • 14 datasets
( Various Video Generation Tasks. Gif credit: MaGViT )
Benchmarks
These leaderboards are used to track progress in Video Generation
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Libraries
Use these libraries to find Video Generation models and implementationsDatasets
UCF101
Kinetics
MSR-VTT
WebVid
LAION-400M
Kinetics-600
How2Sign
BAIR Robot Pushing
Most implemented papers
Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture
FlowGAN generates optical flow, which contains only the edge and motion of the videos to be begerated.
Stochastic Video Generation with a Learned Prior
edenton/svg
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ICML 2018
Sample generations are both varied and sharp, even many frames into the future, and compare favorably to those from existing approaches.
Point-to-Point Video Generation
charlescheng0117/p2pvg
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ICCV 2019
We introduce point-to-point video generation that controls the generation process with two control points: the targeted start- and end-frames.
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
shirgur/hp-vae-gan
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NeurIPS 2020
We consider the task of generating diverse and novel videos from a single video sample.
VideoGPT: Video Generation using VQ-VAE and Transformers
We present VideoGPT: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos.
Video Diffusion Models
lucidrains/make-a-video-pytorch
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Generating temporally coherent high fidelity video is an important milestone in generative modeling research.
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
vqassessment/dover
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ICCV 2023
In light of this, we propose the Disentangled Objective Video Quality Evaluator (DOVER) to learn the quality of UGC videos based on the two perspectives.
Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
showlab/Tune-A-Video
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ICCV 2023
To replicate the success of text-to-image (T2I) generation, recent works employ large-scale video datasets to train a text-to-video (T2V) generator.
FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling
With the availability of large-scale video datasets and the advances of diffusion models, text-driven video generation has achieved substantial progress.
VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
The I2V model is designed to produce videos that strictly adhere to the content of the provided reference image, preserving its content, structure, and style.
