Video Generation
229 papers with code • 15 benchmarks • 14 datasets
( Various Video Generation Tasks. Gif credit: MaGViT )
Libraries
Use these libraries to find Video Generation models and implementationsDatasets
Latest papers with no code
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.
AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks
In the second stage, AnyV2V can plug in any existing image-to-video models to perform DDIM inversion and intermediate feature injection to maintain the appearance and motion consistency with the source video.
Explorative Inbetweening of Time and Space
We introduce bounded generation as a generalized task to control video generation to synthesize arbitrary camera and subject motion based only on a given start and end frame.
Enabling Visual Composition and Animation in Unsupervised Video Generation
We call our model CAGE for visual Composition and Animation for video GEneration.