MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds

التفاصيل البيبلوغرافية
العنوان: MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds
المؤلفون: Lei, Jiahui, Weng, Yijia, Harley, Adam, Guibas, Leonidas, Daniilidis, Kostas
سنة النشر: 2024
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
الوصف: We introduce 4D Motion Scaffolds (MoSca), a neural information processing system designed to reconstruct and synthesize novel views of dynamic scenes from monocular videos captured casually in the wild. To address such a challenging and ill-posed inverse problem, we leverage prior knowledge from foundational vision models, lift the video data to a novel Motion Scaffold (MoSca) representation, which compactly and smoothly encodes the underlying motions / deformations. The scene geometry and appearance are then disentangled from the deformation field, and are encoded by globally fusing the Gaussians anchored onto the MoSca and optimized via Gaussian Splatting. Additionally, camera poses can be seamlessly initialized and refined during the dynamic rendering process, without the need for other pose estimation tools. Experiments demonstrate state-of-the-art performance on dynamic rendering benchmarks.
Comment: project page: https://www.cis.upenn.edu/~leijh/projects/mosca
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2405.17421
رقم الأكسشن: edsarx.2405.17421
قاعدة البيانات: arXiv