VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
Haoxin Chen Menghan Xia Yingqing He Yong Zhang Xiaodong Cun Shaoshu Yang Jinbo Xing Yaofang Liu Qifeng Chen Xintao Wang Chao Weng Ying Shan
Video generation has increasingly gained interest in both academia and
industry. Although commercial tools can generate plausible videos, there is a
limited number of open-source models available for researchers and engineers.
In this work, we introduce two diffusion models for high-quality video
generation, namely text-to-video (T2V) and image-to-video (I2V) models. T2V
models synthesize a video based on a given text input, while I2V models
incorporate an additional image input. Our proposed T2V model can generate
realistic and cinematic-quality videos with a resolution of $1024 \times 576$,
outperforming other open-source T2V models in terms of quality. The I2V model
is designed to produce videos that strictly adhere to the content of the
provided reference image, preserving its content, structure, and style. This
model is the first open-source I2V foundation model capable of transforming a
given image into a video clip while maintaining content preservation
constraints. We believe that these open-source video generation models will
contribute significantly to the technological advancements within the
community.