Frame Interpolation with Consecutive Brownian Bridge Diffusion (2405.05953v7)

Abstract: Recent work in Video Frame Interpolation (VFI) tries to formulate VFI as a diffusion-based conditional image generation problem, synthesizing the intermediate frame given a random noise and neighboring frames. Due to the relatively high resolution of videos, Latent Diffusion Models (LDMs) are employed as the conditional generation model, where the autoencoder compresses images into latent representations for diffusion and then reconstructs images from these latent representations. Such a formulation poses a crucial challenge: VFI expects that the output is deterministically equal to the ground truth intermediate frame, but LDMs randomly generate a diverse set of different images when the model runs multiple times. The reason for the diverse generation is that the cumulative variance (variance accumulated at each step of generation) of generated latent representations in LDMs is large. This makes the sampling trajectory random, resulting in diverse rather than deterministic generations. To address this problem, we propose our unique solution: Frame Interpolation with Consecutive Brownian Bridge Diffusion. Specifically, we propose consecutive Brownian Bridge diffusion that takes a deterministic initial value as input, resulting in a much smaller cumulative variance of generated latent representations. Our experiments suggest that our method can improve together with the improvement of the autoencoder and achieve state-of-the-art performance in VFI, leaving strong potential for further enhancement.

• The paper introduces a two-stage method that uses a latent diffusion model with consecutive Brownian Bridge diffusion to deterministically interpolate video frames.
• It leverages autoencoders to compress high-resolution video data, enabling efficient latent space operations and reducing cumulative variance.
• Experimental results on datasets like Vimeo 90K and DAVIS demonstrate improved perceptual metrics, underscoring its superiority over state-of-the-art approaches.

Frame Interpolation with Consecutive Brownian Bridge Diffusion

The paper "Frame Interpolation with Consecutive Brownian Bridge Diffusion" explores the intricate process of video frame interpolation (VFI) by employing a novel approach using diffusion models. The research presents a unique solution to the inherent challenges of applying diffusion models to VFI, specifically addressing the need for deterministic outputs and dealing with the high-resolution nature of video data.

Overview

Video frame interpolation is a significant task in computer vision, aiming to enhance the temporal resolution of videos by generating intermediate frames. Common methodologies include flow-based and kernel-based methods, each with its own challenges related to motion estimation and computational efficiency. The authors propose a new angle by treating VFI as a diffusion-based conditional image generation problem. This involves synthesizing intermediate frames conditioned on random noise and neighboring frames using latent diffusion models (LDMs).

Methodology

Two-Stage Structure

1. Autoencoder Stage: The method leverages LDMs that utilize autoencoders to convert images into compact latent representations. This allows the model to handle high-resolution data efficiently. The autoencoder's function is akin to image compression, ensuring the diffusion model operates in a manageable latent space.
2. Ground Truth Estimation Stage: The core contribution is using a diffusion model based on consecutive Brownian Bridge diffusion, uniquely constructed to interpolate between three deterministic points: a frame and its surrounding frames. This is proposed to resolve the issue of cumulative variance leading to diverse outputs, which is unsuitable for the deterministic nature required in VFI.

Consecutive Brownian Bridge Diffusion

The consecutive Brownian Bridge diffusion is designed to provide a deterministic journey among three points in latent space, reducing variance and ensuring that repeated sampling yields consistent and accurate frames. This method attempts to blend the determinism needed for VFI with the generative capabilities of diffusion models, achieving a lower cumulative variance compared to traditional diffusion methods.

Experimental Results and Implications

The paper validates the new methodology through extensive experiments on datasets like Vimeo 90K, UCF-101, DAVIS, and SNU-FILM, demonstrating improvements over existing SOTA methods in perceptual quality metrics such as LPIPS and FloLPIPS. The authors also show that the performance of the model scales with improvements in the autoencoder, suggesting a promising direction for future enhancements.

Implications and Future Work

The approach outlined opens potential avenues for future research in both theoretical and practical domains. Theoretically, it encourages further exploration into deterministic diffusion processes that balance the stochastic nature of diffusion models with the need for predictability in VFI tasks. Practically, it highlights the importance of robust autoencoder architectures in handling diverse video content effectively. Future work could focus on refining the autoencoder or examining other forms of conditional control within diffusion frameworks to further enhance interpolation quality.

By proposing a consecutive Brownian Bridge diffusion technique, the paper sets a foundation for more deterministic applications of diffusion models in video processing, likely influencing subsequent approaches in video generation and enhancement tasks beyond interpolation.