Video synthesis has recently made remarkable strides benefiting from the
rapid development of diffusion models. However, it still encounters challenges
in terms of semantic accuracy, clarity and spatio-temporal continuity. They
primarily arise from the scarcity of well-aligned text-video data and the
complex inherent structure of videos, making it difficult for the model to
simultaneously ensure semantic and qualitative excellence. In this report, we
propose a cascaded I2VGen-XL approach that enhances model performance by
decoupling these two factors and ensures the alignment of the input data by
utilizing static images as a form of crucial guidance. I2VGen-XL consists of
two stages: i) the base stage guarantees coherent semantics and preserves
content from input images by using two hierarchical encoders, and ii) the
refinement stage enhances the video's details by incorporating an additional
brief text and improves the resolution to 1280$\times$720. To improve the
diversity, we collect around 35 million single-shot text-video pairs and 6
billion text-image pairs to optimize the model. By this means, I2VGen-XL can
simultaneously enhance the semantic accuracy, continuity of details and clarity
of generated videos. Through extensive experiments, we have investigated the
underlying principles of I2VGen-XL and compared it with current top methods,
which can demonstrate its effectiveness on diverse data. The source code and
models will be publicly available at https://i2vgen-xl.github.io.

提出了 I2VGen-XL 的级联方法，通过将两个因素解耦并利用静态图像作为关键指导，能够同时提高模型的语义准确性、细节的连续性和视频的清晰度。