AI Paper Atlas

Research map / Video / Video generation

Video Diffusion Models

NeurIPS 2022 · Video › Video generation · approach: Efficient video diffusion models · 204 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
add 8 independent image frames per video (joint image-video training) (ablation)text-to-video diffusion · Text-to-video 16x64x64FVD: 202.28 -> 57.84Helped
increase classifier-free guidance weight 1.0 -> 5.0 (ablation)text-to-video diffusion · Text-to-video frameskip 1IS-avg: 10.80 -> 14.80Helped
increase classifier-free guidance weight 1.0 -> 5.0 (ablation)text-to-video diffusion · Text-to-video frameskip 1FVD: 41.65 -> 163.74Hurt
reconstruction guidance instead of replacement for conditioning (ablation)autoregressive extension · 64x64x64 generationFVD: 451.45 -> 136.22Helped
Langevin sampler instead of ancestral (ablation)Video Diffusion · Kinetics-600FVD: 18.6 -> 16.2Helped
video diffusion vs prior GAN/AR methodsvideo generation · BAIR Robot PushingFVD: 86.9 -> 66.92Helped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy