World-Consistent Video-to-Video Synthesis (2007.08509v1)

Abstract: Video-to-video synthesis (vid2vid) aims for converting high-level semantic inputs to photorealistic videos. While existing vid2vid methods can achieve short-term temporal consistency, they fail to ensure the long-term one. This is because they lack knowledge of the 3D world being rendered and generate each frame only based on the past few frames. To address the limitation, we introduce a novel vid2vid framework that efficiently and effectively utilizes all past generated frames during rendering. This is achieved by condensing the 3D world rendered so far into a physically-grounded estimate of the current frame, which we call the guidance image. We further propose a novel neural network architecture to take advantage of the information stored in the guidance images. Extensive experimental results on several challenging datasets verify the effectiveness of our approach in achieving world consistency - the output video is consistent within the entire rendered 3D world. https://nvlabs.github.io/wc-vid2vid/

• The paper introduces guidance images as a novel approach to maintain world consistency in video synthesis.
• The method leverages a multi-SPADE architecture and 3D structure estimates to enhance temporal stability and reduce flickering artifacts.
• Experimental results on datasets like Cityscapes and MannequinChallenge show improved FID, mIOU, and pixel accuracy compared to existing methods.

Overview of World-Consistent Video-to-Video Synthesis

The paper "World-Consistent Video-to-Video Synthesis" addresses critical challenges in video-to-video synthesis, particularly the difficulty in maintaining world consistency across frames. Existing methods often achieve short-term temporal consistency but fail to preserve long-term spatial and temporal coherence necessary for rendering a consistent 3D world representation. The authors propose a novel framework that integrates guidance images to address these limitations, arguing that traditional optical flow methods fall short in maintaining continuity across extended video sequences.

The core contribution is the introduction of guidance images, which are physically-grounded approximations of what future video frames should look like based on the history of generated frames. This is achieved through condensing scenes into these guidance images derived from 3D structure estimates, and leveraging these as additional input during video synthesis. The method harnesses a multi-SPADE architecture allowing multiple conditional inputs, improving temporal stability and consistency across viewpoints by considering all previously rendered frames.

Experimental Design and Results

The proposed method was evaluated against strong baselines, including vid2vid and SPADE, across several datasets like Cityscapes, MannequinChallenge, and ScanNet. Metrics such as Frechet Inception Distance (FID), mean Intersection-Over-Union (mIOU), and Pixel Accuracy (P.A.) were used to quantify image quality, realism, and temporal stability. Results indicated that the proposed method consistently outperformed existing approaches with improvements in world consistency and reduced flickering artifacts. Ours (with World Consistency) achieved better scores than Ours w/o World Consistency, showcasing the efficacy of guidance images.

A subjective human evaluation corroborated these findings, showing a marked preference for the proposed method over baseline approaches concerning both image realism and temporal smoothness.

Theoretical and Practical Implications

The research advances the field by addressing world consistency—a crucial but often overlooked aspect in video synthesis. The ability to create video sequences that maintain spatial consistency across different views and times opens pathways for applications involving interactive and immersive environments, such as augmented reality and virtual simulations. The application of guidance images introduces a scalable way to control video synthesis quality conditional on past generated data, potentially influencing future AI-driven graphics engines.

Speculations on Future Developments

Future advancements may explore extensions of this framework in scenarios involving dynamic lighting conditions or objects with complex motions and variability over time. Improving 3D registration techniques to refine guidance image generation will enhance consistency further. Such developments could refine the conceptual framework for neural rendering and synthesis, leading to more robust applications across various domains like gaming, simulation, and digital content creation.

In summary, the paper rigorously substantiates the need and methodology for achieving long-term consistency in video-to-video synthesis, providing a strategic enhancement to current practices and laying the groundwork for future explorations in neural video rendering.