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February 5, 2025
Generative AI models have made impressive progress in video creation in recent years. However, these models continue to struggle with the realistic representation of motion, dynamics, and physical laws. A new framework called VideoJAM promises to remedy this by improving the coherence of motion in generated videos.
Conventional generative video models focus primarily on pixel reconstruction, leading to a prioritization of visual quality at the expense of motion accuracy. VideoJAM addresses this problem by providing the models with an effective understanding of motion. This is achieved by learning a joint representation of appearance and motion.
The framework consists of two central components. During training, the model is trained not only to generate the pixels of the video but also to predict the associated motion from a shared representation. In the application phase, a mechanism called "Inner-Guidance" is used. This utilizes the motion predicted by the model itself as a dynamic guidance signal to steer the generation towards more coherent motion.
A notable advantage of VideoJAM is its broad applicability. The framework can be integrated into existing video models with minimal adjustments, without the need to modify the training data or scale the model. Initial results show that VideoJAM significantly improves the state of the art in terms of motion coherence and even surpasses powerful proprietary models. At the same time, it increases the perceived visual quality of the generated videos.
The developers of VideoJAM emphasize that appearance and motion can complement each other and, when effectively integrated, improve both the visual quality and the coherence of video generation. These findings could represent an important step in the development of more realistic and convincing generative video models.
For Mindverse, a German company specializing in AI-powered content creation, these developments are of particular interest. Mindverse offers an all-in-one platform for AI texts, images, research, and more. As an AI partner, the company also develops customized solutions such as chatbots, voicebots, AI search engines, and knowledge systems. The improvement of video generation through frameworks like VideoJAM opens up new possibilities for content creation and could drive the development of innovative applications in the field of artificial intelligence.
The research results of VideoJAM underscore the potential of AI models to master complex tasks like video generation. By integrating an understanding of motion and focusing on coherence, more realistic and dynamic video content is within reach. Future developments in this area will show how this technology will further revolutionize the creative possibilities of content creation.
Bibliographie: Chefer, H., Singer, U., Zohar, A., Kirstain, Y., Polyak, A., Taigman, Y., Wolf, L., & Sheynin, S. (2025). VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models. arXiv preprint arXiv:2502.02492. https://hila-chefer.github.io/videojam-paper.github.io/ https://chatpaper.com/chatpaper/zh-CN?id=4&date=1738684800&page=1 https://arxiv.org/abs/2411.08328 https://www.researchgate.net/publication/334434830_Stylizing_video_by_example https://huggingface.co/papers/2411.08328 https://arxiv.org/abs/2211.12748 https://x.com/hila_chefer https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/06030.pdf https://github.com/AlonzoLeeeooo/awesome-video-generation https://www.vdb.org/sites/default/files/2020-04/Rewind_VDB_July2009%202.pdf