  Chinese landscape painting is a gem of Chinese cultural and artistic heritage
that showcases the splendor of nature through the deep observations and
imaginations of its painters. Limited by traditional techniques, these artworks
were confined to static imagery in ancient times, leaving the dynamism of
landscapes and the subtleties of artistic sentiment to the viewer's
imagination. Recently, emerging text-to-video (T2V) diffusion methods have
shown significant promise in video generation, providing hope for the creation
of dynamic Chinese landscape paintings. However, challenges such as the lack of
specific datasets, the intricacy of artistic styles, and the creation of
extensive, high-quality videos pose difficulties for these models in generating
Chinese landscape painting videos. In this paper, we propose CLV-HD (Chinese
Landscape Video-High Definition), a novel T2V dataset for Chinese landscape
painting videos, and ConCLVD (Controllable Chinese Landscape Video Diffusion),
a T2V model that utilizes Stable Diffusion. Specifically, we present a motion
module featuring a dual attention mechanism to capture the dynamic
transformations of landscape imageries, alongside a noise adapter to leverage
unsupervised contrastive learning in the latent space. Following the generation
of keyframes, we employ optical flow for frame interpolation to enhance video
smoothness. Our method not only retains the essence of the landscape painting
imageries but also achieves dynamic transitions, significantly advancing the
field of artistic video generation. The source code and dataset are available
at https://anonymous.4open.science/r/ConCLVD-EFE3.
