Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction (2504.07961v1)

Abstract: We introduce Geo4D, a method to repurpose video diffusion models for monocular 3D reconstruction of dynamic scenes. By leveraging the strong dynamic prior captured by such video models, Geo4D can be trained using only synthetic data while generalizing well to real data in a zero-shot manner. Geo4D predicts several complementary geometric modalities, namely point, depth, and ray maps. It uses a new multi-modal alignment algorithm to align and fuse these modalities, as well as multiple sliding windows, at inference time, thus obtaining robust and accurate 4D reconstruction of long videos. Extensive experiments across multiple benchmarks show that Geo4D significantly surpasses state-of-the-art video depth estimation methods, including recent methods such as MonST3R, which are also designed to handle dynamic scenes.

• The paper introduces Geo4D, a novel method for geometric 4D scene reconstruction by adapting pre-trained video diffusion models.
• Geo4D uses multi-modal geometric representations (depth, point, ray maps) and a multi-modal alignment optimization for coherent reconstruction.
• Trained solely on synthetic data, Geo4D demonstrates superior performance over state-of-the-art methods in video depth and camera pose estimation on benchmark datasets.

Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction

The paper titled "Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction" presents a novel approach for reconstructing dynamic 4D scenes using a diffusion model, extending the capabilities of traditional 3D reconstruction techniques. The authors exploit the strengths of pre-trained video diffusion models and adapt them for the task of 4D reconstruction, emphasizing how robust motion priors and geometric understanding encapsulated in these models can be harnessed for reconstructing complex scenes from monocular videos.

Methodology Overview

Geo4D utilizes a pre-existing video diffusion model named DynamiCrafter as a foundation. The model is tailored to perform 4D geometric tasks by integrating multi-modal geometric representations such as depth maps, point maps, and ray maps. Specifically, the paper describes the following key innovations:

1. Multi-Modal Representation: The authors extend the model to predict multiple geometric modalities—depth maps providing robust range information, point maps offering viewpoint-invariant 3D representation, and ray maps capturing the geometric structure, which collectively enhance the accuracy and coherence of the reconstructed scenes.
2. Geometric Fusion and Alignment: A novel multi-modal alignment optimization is introduced to fuse the outputs from these modalities, ensuring a globally coherent reconstruction across long video sequences. The strategy aligns and leverages these disparate sources to mitigate noise and improve reconstruction fidelity.
3. Synthetic Data Utilization: The model is exclusively trained on synthetic datasets, showcasing the capability of zero-shot generalization to real-world sequences. This is a significant stride in reducing dependency on vast labeled real-world data.

Experimental Analysis

Geo4D demonstrates superior performance across several benchmarks, significantly outperforming state-of-the-art methods in video depth estimation and camera pose estimation. Notably, the model achieved a remarkable reduction in the absolute relative error (Abs Rel) on the KITTI dataset compared to competing approaches, underscoring the quantitative improvements gained through its multi-modal architecture and the sophisticated alignment methodology.

• Video Depth Estimation: Geo4D's performance on the Sintel, KITTI, and Bonn datasets reflects its robust depth prediction capabilities, yielding superior accuracy over recent models like MonST3R and DepthCrafter.
• Camera Pose Estimation: The evaluation showed competitive or enhanced estimation of camera motion parameters, bolstering Geo4D's utility in dynamic scene reconstruction.

Implications and Future Directions

The implications of Geo4D are multi-fold, bridging the gap between static 3D reconstruction and dynamic, time-variant scenes. By effectively leveraging the motion and scene priors in pre-trained diffusion models, Geo4D sets a precedent for future developments in interactive applications like video editing, virtual reality, and robotics, where real-time 4D scene understanding is essential.

Furthermore, the proposed framework opens avenues for integrating diffusion-based generative models into generalized video understanding tasks, potentially leading to video foundation models that inherently grasp 4D geometry—transforming not just static understanding but dynamic predictions in AI-driven solutions.

Future work could explore refining the point map encoder-decoder systems for enhanced fidelity and tackling complex real-world dynamics through hybrid data synthesis strategies, blending synthetic and real-world nuances to further optimize generalization and performance in open-world environments.