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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Video Generation models and implementationsDatasets
UCF101
Kinetics
MSR-VTT
WebVid
LAION-400M
Kinetics-600
How2Sign
BAIR Robot Pushing
Latest papers with no code
Motion Inversion for Video Customization
In this research, we present a novel approach to motion customization in video generation, addressing the widespread gap in the thorough exploration of motion representation within video generative models.
Frame by Familiar Frame: Understanding Replication in Video Diffusion Models
In our paper, we present a systematic investigation into the phenomenon of sample replication in video diffusion models.
A Review of Multi-Modal Large Language and Vision Models
Large Language Models (LLMs) have recently emerged as a focal point of research and application, driven by their unprecedented ability to understand and generate text with human-like quality.
TC4D: Trajectory-Conditioned Text-to-4D Generation
We learn local deformations that conform to the global trajectory using supervision from a text-to-video model.
Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields
It is composed of a denoising diffusion probabilistic model (DDPM) generating high-fidelity synthetic cell microscopy images and a flow prediction model (FPM) predicting the non-rigid transformation between consecutive video frames.
Tutorial on Diffusion Models for Imaging and Vision
The goal of this tutorial is to discuss the essential ideas underlying the diffusion models.
A Survey on Long Video Generation: Challenges, Methods, and Prospects
Video generation is a rapidly advancing research area, garnering significant attention due to its broad range of applications.
TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models
Next, TRIP executes a residual-like dual-path scheme for noise prediction: 1) a shortcut path that directly takes image noise prior as the reference noise of each frame to amplify the alignment between the first frame and subsequent frames; 2) a residual path that employs 3D-UNet over noised video and static image latent codes to enable inter-frame relational reasoning, thereby easing the learning of the residual noise for each frame.
Opportunities and challenges in the application of large artificial intelligence models in radiology
Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development.
Spectral Motion Alignment for Video Motion Transfer using Diffusion Models
The evolution of diffusion models has greatly impacted video generation and understanding.
