VideoGen: A Reference-Guided Latent Diffusion Approach for High
  Definition Text-to-Video Generation
Authors: name: Xin Li; name: Wenqing Chu; name: Ye Wu; name: Weihang Yuan; name: Fanglong Liu; name: Qi Zhang; name: Fu Li; name: Haocheng Feng; name: Errui Ding; name: Jingdong Wang
Published: 2023-09-01 11:14:43

Abstract: In this paper, we present VideoGen, a text-to-video generation approach,
which can generate a high-definition video with high frame fidelity and strong
temporal consistency using reference-guided latent diffusion. We leverage an
off-the-shelf text-to-image generation model, e.g., Stable Diffusion, to
generate an image with high content quality from the text prompt, as a
reference image to guide video generation. Then, we introduce an efficient
cascaded latent diffusion module conditioned on both the reference image and
the text prompt, for generating latent video representations, followed by a
flow-based temporal upsampling step to improve the temporal resolution.
Finally, we map latent video representations into a high-definition video
through an enhanced video decoder. During training, we use the first frame of a
ground-truth video as the reference image for training the cascaded latent
diffusion module. The main characterises of our approach include: the reference
image generated by the text-to-image model improves the visual fidelity; using
it as the condition makes the diffusion model focus more on learning the video
dynamics; and the video decoder is trained over unlabeled video data, thus
benefiting from high-quality easily-available videos. VideoGen sets a new
state-of-the-art in text-to-video generation in terms of both qualitative and
quantitative evaluation.