Predicting 3D representations for Dynamic Scenes

• URL: http://arxiv.org/abs/2501.16617v1
• Date: Tue, 28 Jan 2025 01:31:15 GMT
• Title: Predicting 3D representations for Dynamic Scenes
• Authors: Di Qi, Tong Yang, Beining Wang, Xiangyu Zhang, Wenqiang Zhang,
• Abstract summary: We present a novel framework for dynamic radiance field prediction given monocular video streams.<n>Our method goes a step further by generating explicit 3D representations of the dynamic scene.<n>We find that our approach emerges capabilities for geometry and semantic learning.
• Score: 29.630985082164383
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a novel framework for dynamic radiance field prediction given monocular video streams. Unlike previous methods that primarily focus on predicting future frames, our method goes a step further by generating explicit 3D representations of the dynamic scene. The framework builds on two core designs. First, we adopt an ego-centric unbounded triplane to explicitly represent the dynamic physical world. Second, we develop a 4D-aware transformer to aggregate features from monocular videos to update the triplane. Coupling these two designs enables us to train the proposed model with large-scale monocular videos in a self-supervised manner. Our model achieves top results in dynamic radiance field prediction on NVIDIA dynamic scenes, demonstrating its strong performance on 4D physical world modeling. Besides, our model shows a superior generalizability to unseen scenarios. Notably, we find that our approach emerges capabilities for geometry and semantic learning.

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