DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidance

Abstract

Image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods frequently integrate semantic information from images or simply concatenate images, which often leads to low fidelity and flickering in the generated videos. To tackle these problems, we propose a high-fidelity image-to-video generation method by devising a frame retention branch based on a pre-trained video diffusion model, named DreamVideo. Our DreamVideo perceives the reference image via convolution layers and concatenates the features with the noisy latents as model input. By this means, the details of the reference image can be preserved to the greatest extent. In addition, by incorporating the designed double-condition classifier-free guidance, DreamVideo can generate high-quality videos of different actions by providing varying prompt texts. We conduct comprehensive experiments on the public datasets, and both quantitative and qualitative results indicate that our method outperforms the state-of-the-art method.