Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos (2412.03526v2)

Abstract: Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle with generalizability across diverse environments and fail to effectively handle dynamic content. We present BTimer (short for BulletTimer), the first motion-aware feed-forward model for real-time reconstruction and novel view synthesis of dynamic scenes. Our approach reconstructs the full scene in a 3D Gaussian Splatting representation at a given target ('bullet') timestamp by aggregating information from all the context frames. Such a formulation allows BTimer to gain scalability and generalization by leveraging both static and dynamic scene datasets. Given a casual monocular dynamic video, BTimer reconstructs a bullet-time scene within 150ms while reaching state-of-the-art performance on both static and dynamic scene datasets, even compared with optimization-based approaches.

• The paper presents BulletTimer, a real-time feed-forward model that applies a bullet-time formulation with 3D Gaussian Splatting for dynamic scene reconstruction.
• The model efficiently aggregates context frames to unify static and dynamic reconstructions, rendering scenes in just 150ms.
• Experimental results demonstrate competitive performance on standard benchmarks, indicating potential for AR/VR and real-time video editing applications.

An Expert Review of "Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos"

The paper "Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos" presents BulletTimer, a pioneering model for real-time reconstruction and novel view synthesis of dynamic scenes from monocular videos. This advancement addresses the significant challenge of dynamic scene reconstruction, which has traditionally been impeded by limitations in capturing and rendering scenes with variable motion from minimal observations.

Key Contributions

1. Dynamic Scene Reconstruction: BulletTimer stands as the first feed-forward, motion-aware model aimed at both real-time reconstruction and novel view synthesis of dynamic scenes using a 3D Gaussian Splatting (3DGS) framework. This framework aligns bullet-time ("frozen" 3D scene at a fixed timestamp) to monocular inputs, making it scalable across both static and dynamic datasets.
2. Bullet-Time Formulation: The approach leverages a bullet-time timestamp to unify static and dynamic reconstructions. This formulation enables the model to adaptively focus on the desired scene instance by aggregating context frames without requiring extensive multiview or structural data typically needed by optimization-based methods.
3. Speed and Performance: BulletTimer achieves reconstruction efficiency, rendering a 3DGS scene in just 150ms, and outperforms many traditional per-scene optimization methods in static and dynamic benchmarks. The model shows competitive advantages on commonly used datasets such as the NVIDIA Dynamic Scene Dataset, with demonstrated flexibility in handling complex motion scenarios.
4. Novel Time Enhancer (NTE): For scenes with fast dynamics, the NTE module enhances temporal coherence by predicting intermediate frames necessary for a fluid transition between observed states. This module operates efficiently, contributing minimal computational overhead while improving frame interpolation.

Experimental Evaluation

The rigorous experimental evaluation showcases BulletTimer's proficiency across multiple benchmarks, including datasets composed of synchronized multi-camera captures and internet videos annotated for real-world dynamic scenes. It successfully matches or surpasses the performance of existing optimization-heavy methodologies, presenting strong results in standard metrics like PSNR, SSIM, and LPIPS.

Implications and Future Directions

Theoretical Implications: The integration of 3DGS and bullet-time timestamping offers a path forward for dynamic scene rendering. By avoiding traditional optimization and leveraging generalized data priors, BulletTimer enhances depth prediction and geometric accuracy in scenarios typically restrained by real-time processing constraints.

Practical Implications: The capacity to process monocular inputs into high-fidelity 3D renderings has immediate applications in areas of AR/VR, real-time video editing, and simulation environments. This advance suggests that bullet-time formulations can effectively circumvent the inefficiencies of multi-view dependencies, streamlining content creation pipelines significantly.

Speculative Future Prospects: While BulletTimer adeptly handles present limitations in motion complexity and scene dynamics, future exploration of generative models could further extend scene understanding, facilitating enhanced view extrapolation and potentially generalizing to unseen domain variations. Enhanced interpretability through improved scene geometry could provide additional avenues for integrating with broader AI frameworks that necessitate dynamic real-world scene manipulation.

In conclusion, "Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos" introduces a substantial leap forward in the field of dynamic scene reconstruction. The innovative bullet-time approach, coupled with real-time operational speed, positions BulletTimer as a transformative tool for handling complex visual information in real-world applications. This paper lays a robust foundation for subsequent investigations into efficient, scalable 3D reconstruction technologies across diverse environments.