arXiv:2404.12903v1 Announce Type: new
Abstract: 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.
Analysis of the Content: Chinese Landscape Painting Videos
This article discusses the creation of dynamic Chinese landscape painting videos using text-to-video (T2V) diffusion methods. It highlights the limitations of traditional techniques that confined these artworks to static imagery, and the potential of T2V methods to bring them to life. The article introduces CLV-HD, a novel T2V dataset for Chinese landscape painting videos, and ConCLVD, a T2V model that utilizes Stable Diffusion. It also presents a motion module with a dual attention mechanism and a noise adapter to capture dynamic transformations and enhance video smoothness.
Multi-disciplinary Nature and Relation to Multimedia Information Systems
The creation of dynamic Chinese landscape painting videos involves a multi-disciplinary approach. It combines elements of art, technology, and computer science to generate videos that showcase the beauty and dynamism of Chinese landscapes. This multi-disciplinary nature is closely related to the field of multimedia information systems.
Multimedia information systems involve the storage, retrieval, and manipulation of different types of media, such as text, images, audio, and video. The T2V methods and techniques discussed in this article are a prime example of how multimedia information systems can be applied to generate dynamic videos from static imagery. By leveraging text, algorithms, and artistic techniques, these systems enhance the user experience and provide new ways of interacting with visual content.
Connection to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The concept of creating dynamic Chinese landscape painting videos through T2V methods has a direct connection to animations and virtual realities. Animations involve the manipulation of static images to create the illusion of motion. The T2V techniques described in the article take this concept a step further by generating videos that simulate the experience of exploring a Chinese landscape painting in motion.
Artificial reality, which encompasses augmented reality and virtual reality, also relates to the content of this article. Augmented reality overlays digital content onto the real world, while virtual reality provides immersive experiences in entirely virtual environments. The creation of dynamic Chinese landscape painting videos can be seen as a form of augmented reality, where the videos add a layer of dynamic content to static paintings. These videos can also be part of virtual reality experiences, where users can explore and interact with virtual landscapes inspired by Chinese art.
Expert Insights: Advancements and Challenges
The advancements discussed in this article, such as CLV-HD and ConCLVD, show promising progress in the field of artistic video generation. These techniques enable the creation of dynamic Chinese landscape painting videos that capture the essence of the artworks while providing a visually engaging experience.
However, there are still challenges to overcome. One major difficulty is the lack of specific datasets for Chinese landscape painting videos. Creating a comprehensive and diverse dataset that accurately represents the intricacies of Chinese artistic styles is crucial for training and evaluating T2V models. It requires collaboration between artists, researchers, and experts in cultural heritage.
Another challenge lies in the creation of high-quality videos. Generating high-resolution videos that maintain the fidelity of the original artworks requires advanced algorithms and computational resources. Finding the right balance between preserving artistic sentiment and achieving dynamic transitions is an ongoing area of research.
Despite these challenges, the advancements in T2V methods and the creation of dynamic Chinese landscape painting videos open up possibilities for further exploration. Integrating other forms of media, such as audio and interactive elements, could enhance the immersive experience and provide even more engaging interactions with these artistic representations.
In conclusion, the creation of dynamic Chinese landscape painting videos using T2V methods represents a significant advancement in the field of artistic video generation. This multi-disciplinary approach connects with the wider field of multimedia information systems and relates to concepts like animations, artificial reality, augmented reality, and virtual realities. While challenges exist, further advancements and collaborations have the potential to revolutionize the way we experience and preserve cultural heritage.