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November 28, 2024
The creation of 4D scenes, i.e., dynamic 3D models, from simple monocular videos presents a significant challenge in computer graphics. A promising approach to solving this problem is CAT4D, a new method based on multi-view video diffusion models.
CAT4D leverages the power of video diffusion models trained on a combination of various datasets. These models enable the synthesis of new views from arbitrary camera perspectives and at specific points in time. Through a novel sampling approach, CAT4D can transform a single monocular video into a multi-view video. This allows for robust 4D reconstruction by optimizing a deformable 3D Gaussian representation.
The process begins with a monocular video as input. The trained diffusion model then generates multiple views of the object or scene from various virtual camera perspectives. These generated views are subsequently used to perform a 3D reconstruction. The optimization of a deformable 3D Gaussian representation allows the capture of the dynamic changes of the scene over time.
CAT4D shows promising results in benchmarks for novel view synthesis and dynamic scene reconstruction. The method opens up creative possibilities for 4D scene generation from real or generated videos. Potential applications range from the creation of special effects in movies and video games to virtual reality and augmented reality. CAT4D could also provide valuable services in areas such as architecture, design, and medicine.
The ability to generate a dynamic 3D scene from a single monocular video significantly simplifies the process of 4D modeling. Previous methods often required complex and costly procedures for capturing 3D data. CAT4D offers an efficient and accessible alternative.
Despite the promising results, research in the field of 4D generation is still in its early stages. Improving the quality of the generated 3D models, reducing the computational effort, and expanding the range of applications are important goals for future developments. The integration of semantic information and the consideration of complex movements and interactions within the scenes are further research areas.
The development of more efficient training methods for the diffusion models and the optimization of the 3D reconstruction processes are also important aspects for further increasing the performance of CAT4D.
Compared to other methods of 4D generation, CAT4D offers several advantages. The use of multi-view video diffusion models enables a more detailed and consistent reconstruction of the scenes. The ability to generate new views from arbitrary perspectives expands the creative possibilities. The optimization of a deformable 3D representation allows the capture of complex movements and deformations.
Bibliographie: Wu, R., Gao, R., Poole, B., Trevithick, A., Zheng, C., Barron, J. T., & Holynski, A. (2024). CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models. arXiv preprint arXiv:2411.18613. Zhang, H., Chen, X., Wang, Y., Liu, X., Wang, Y., & Qiao, Y. (2024). 4Diffusion: Multi-view Video Diffusion Model for 4D Generation. arXiv preprint arXiv:2405.20674. Gao, R., Holynski, A., Henzler, P., Brussee, A., Martin-Brualla, R., Srinivasan, P., Barron, J. T., & Poole, B. (2024). CAT3D: Create Anything in 3D with Multi-View Diffusion Models. arXiv preprint arXiv:2405.10314. aejion/4Diffusion. (n.d.). GitHub. Retrieved from https://github.com/aejion/4Diffusion Gao, R., Holynski, A., Henzler, P., Brussee, A., Martin-Brualla, R., Srinivasan, P. P., Barron, J. T., & Poole, B. (2024). CAT3D: Create Anything in 3D with Multi-View Diffusion Models. Retrieved from https://cat3d.github.io/ Gao, R., Holynski, A., Henzler, P., Brussee, A., Martin-Brualla, R., Srinivasan, P. P., Barron, J. T., & Poole, B. (2024). CAT3D: Create Anything in 3D with Multi-View Diffusion Models. In Advances in Neural Information Processing Systems. Retrieved from https://openreview.net/forum?id=TFZlFRl9Ks ChatPaper. (n.d.). CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models. Retrieved from https://www.chatpaper.com/chatpaper/fr?id=4&date=1732723200&page=1 Zhang, H., Chen, X., Wang, Y., Liu, X., Wang, Y., & Qiao, Y. (2024). 4Diffusion: Multi-view Video Diffusion Model for 4D Generation. ResearchGate. Retrieved from https://www.researchgate.net/publication/381109023_4Diffusion_Multi-view_Video_Diffusion_Model_for_4D_Generation Gao, R., Holynski, A., Henzler, P., Brussee, A., Martin-Brualla, R., Srinivasan, P., Barron, J. T., & Poole, B. (2024). CAT3D: Create Anything in 3D with Multi-View Models. Semantic Scholar. Retrieved from https://www.semanticscholar.org/paper/CAT3D%3A-Create-Anything-in-3D-with-Multi-View-Models-Gao-Holynski/4987a76781f299be64ac43419c8b489ca1f4515b Bao, J., Li, X., & Yang, M.-H. (2024). Tex4D: Zero-shot 4D Scene Texturing with Video Diffusion Models. arXiv preprint arXiv:2410.10821. Retrieved from https://tex4d.github.io/