Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation
Inspired by the remarkable success of Latent Diffusion Models (LDMs) for
image synthesis, we study LDM for text-to-video generation, which is a
formidable challenge due to the computational and memory constraints during
both model training and inference. A single LDM is usually only capable of
generating a very limited number of video frames. Some existing works focus on
separate prediction models for generating more video frames, which suffer from
additional training cost and frame-level jittering, however. In this paper, we
propose a framework called "Reuse and Diffuse" dubbed VidRD to
produce more frames following the frames already generated by an LDM.
Conditioned on an initial video clip with a small number of frames, additional
frames are iteratively generated by reusing the original latent features and
following the previous diffusion process. Besides, for the autoencoder used for
translation between pixel space and latent space, we inject temporal layers
into its decoder and fine-tune these layers for higher temporal consistency. We
also propose a set of strategies for composing video-text data that involve
diverse content from multiple existing datasets including video datasets for
action recognition and image-text datasets. Extensive experiments show that our
method achieves good results in both quantitative and qualitative evaluations.
Our project page is available
https://anonymous0x233.github.io/ReuseAndDiffuse/{here}.
64fa8ee9da4429f25dce324c
Jiaxi Gu
64fa8ee9da4429f25dce324d
Shicong Wang
64fa8ee9da4429f25dce324e
Haoyu Zhao
64fa8ee9da4429f25dce324f
Tianyi Lu
64fa8ee9da4429f25dce3250
Xing Zhang
64fa8ee9da4429f25dce3251
Zuxuan Wu
64fa8ee9da4429f25dce3252
Songcen Xu
64fa8ee9da4429f25dce3253
Wei Zhang
64fa8ee9da4429f25dce3254
Yu-Gang Jiang
64fa8ee9da4429f25dce3255
Hang Xu
Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation
Inspired by the remarkable success of Latent Diffusion Models (LDMs) for
image synthesis, we study LDM for text-to-video generation, which is a
formidable challenge due to the computational and memory constraints during
both model training and inference. A single LDM is usually only capable of
generating a very limited number of video frames. Some existing works focus on
separate prediction models for generating more video frames, which suffer from
additional training cost and frame-level jittering, however. In this paper, we
propose a framework called "Reuse and Diffuse" dubbed VidRD to
produce more frames following the frames already generated by an LDM.
Conditioned on an initial video clip with a small number of frames, additional
frames are iteratively generated by reusing the original latent features and
following the previous diffusion process. Besides, for the autoencoder used for
translation between pixel space and latent space, we inject temporal layers
into its decoder and fine-tune these layers for higher temporal consistency. We
also propose a set of strategies for composing video-text data that involve
diverse content from multiple existing datasets including video datasets for
action recognition and image-text datasets. Extensive experiments show that our
method achieves good results in both quantitative and qualitative evaluations.
Our project page is available
https://anonymous0x233.github.io/ReuseAndDiffuse/{here}.
A Reuse and Diffuse framework (VidRD) enhances text-to-video generation by iteratively adding frames to initial video clips using latent features and temporal consistency enhancements.
Latent Diffusion Models (LDMs) text-to-video generation diffusion models latent space pixel space temporal layers autoencoder video-text data video datasets image-text datasets temporal consistency