NoiseController: Towards Consistent Multi-view Video Generation via Noise Decomposition and Collaboration (2504.18448v1)

Abstract: High-quality video generation is crucial for many fields, including the film industry and autonomous driving. However, generating videos with spatiotemporal consistencies remains challenging. Current methods typically utilize attention mechanisms or modify noise to achieve consistent videos, neglecting global spatiotemporal information that could help ensure spatial and temporal consistency during video generation. In this paper, we propose the NoiseController, consisting of Multi-Level Noise Decomposition, Multi-Frame Noise Collaboration, and Joint Denoising, to enhance spatiotemporal consistencies in video generation. In multi-level noise decomposition, we first decompose initial noises into scene-level foreground/background noises, capturing distinct motion properties to model multi-view foreground/background variations. Furthermore, each scene-level noise is further decomposed into individual-level shared and residual components. The shared noise preserves consistency, while the residual component maintains diversity. In multi-frame noise collaboration, we introduce an inter-view spatiotemporal collaboration matrix and an intra-view impact collaboration matrix , which captures mutual cross-view effects and historical cross-frame impacts to enhance video quality. The joint denoising contains two parallel denoising U-Nets to remove each scene-level noise, mutually enhancing video generation. We evaluate our NoiseController on public datasets focusing on video generation and downstream tasks, demonstrating its state-of-the-art performance.

An Expert Review of "NoiseController: Towards Consistent Multi-view Video Generation via Noise Decomposition and Collaboration"

The paper "NoiseController: Towards Consistent Multi-view Video Generation via Noise Decomposition and Collaboration" presents a novel approach for addressing the challenges associated with generating high-quality and spatially-temporally consistent videos, specifically targeting applications such as the autonomous driving domain. The work introduces the NoiseController framework, which significantly enhances video generation consistency by employing sophisticated noise manipulation techniques.

Core Contributions

NoiseController is composed of three key components designed to address spatiotemporal inconsistency in video generation:

1. Multi-Level Noise Decomposition: This step involves breaking down initial noise into scene-level components, specifically foreground and background noise, and further into shared and residual individual-level components. The shared noise contributes to consistency, while residual noise aids in maintaining diversity across video frames and views.
2. Multi-Frame Noise Collaboration: The collaboration mechanism utilizes inter-view and intra-view matrices to capture historical impacts and cross-view effects. This aspect of the framework is essential for maintaining the quality of multi-view video, as it ensures that spatial and temporal inconsistencies are minimized across generated outputs.
3. Joint Denoising Network: Implementing parallel denoising networks for foreground and background noise ensures that the generation process adheres to the principles of diffusion models, allowing a more controllable and efficient outcome.

Numerical Results and Claims

Experimental results demonstrate that NoiseController achieves state-of-the-art performance on public datasets, surpassing existing methods in generating consistent videos. The paper reports significant improvements in Fréchet Video Distance (FVD) and Fréchet Inception Distance (FID), key metrics in evaluating video quality. NoiseController achieved an FVD score of 122.9 and an FID score of 14.65 on the nuScenes dataset, marking substantial advancements over competing methods.

Implications and Future Directions

The practical implications of this research are profound, particularly in autonomous vehicle technology where multi-view video consistency is crucial for reliable perception and decision-making systems. The techniques introduced offer a pathway toward more flexible and robust video generation frameworks that can adapt to varying requirements and constraints in real-time applications.

Furthermore, on a theoretical level, this research contributes to a deeper understanding of noise manipulation and decomposition within the field of video diffusion models. The layered decomposition strategy could inspire similar approaches in related fields, such as image restoration and enhancement, where initial noise management remains a fundamental challenge.

Future exploration could include refining the decomposition and collaboration strategies, exploring larger and more complex environments, or integrating the framework with other sensor modalities for enhanced situational awareness in autonomous systems. Additionally, the scalability and efficacy of NoiseController in different hardware architectures would be worth investigating to ensure broader applicability in the industry.

In summary, the paper proposes an innovative solution to a persistent problem in video generation, effectively leveraging noise manipulation and collaboration techniques to improve consistency. NoiseController stands as a promising development with significant theoretical and practical implications.