Unconditional Video Generation
9 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Unconditional Video Generation
| Trend | Dataset | Best Model | Paper | Code | Compare |
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CelebV-HQ
Most implemented papers
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.
MOSO: Decomposing MOtion, Scene and Object for Video Prediction
Experimental results demonstrate that our method achieves new state-of-the-art performance on five challenging benchmarks for video prediction and unconditional video generation: BAIR, RoboNet, KTH, KITTI and UCF101.
Latent Neural Differential Equations for Video Generation
Zasder3/Latent-Neural-Differential-Equations-for-Video-Generation
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Generative Adversarial Networks have recently shown promise for video generation, building off of the success of image generation while also addressing a new challenge: time.
CelebV-HQ: A Large-Scale Video Facial Attributes Dataset
Large-scale datasets have played indispensable roles in the recent success of face generation/editing and significantly facilitated the advances of emerging research fields.
MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs
We present MotionVideoGAN, a novel video generator synthesizing videos based on the motion space learned by pre-trained image pair generators.
Video Diffusion Models with Local-Global Context Guidance
exisas/lgc-vd
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We construct a local-global context guidance strategy to capture the multi-perceptual embedding of the past fragment to boost the consistency of future prediction.
DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles
taldatech/ddlp
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We propose a new object-centric video prediction algorithm based on the deep latent particle (DLP) representation.
StyleInV: A Temporal Style Modulated Inversion Network for Unconditional Video Generation
johannwyh/styleinv
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ICCV 2023
In this paper, we introduce a novel motion generator design that uses a learning-based inversion network for GAN.
StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN
We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN.
