LatentColorization: Latent Diffusion-Based Speaker Video Colorization
This addresses the challenge of flickering and abrupt color transitions in video colorization, which is important for applications like film restoration, but it is incremental as it builds on existing diffusion models.
The paper tackled the problem of temporal inconsistency in video colorization by using a fine-tuned latent diffusion model with a temporal consistency mechanism, resulting in strong improvements on image quality metrics and user preference over existing methods.
While current research predominantly focuses on image-based colorization, the domain of video-based colorization remains relatively unexplored. Most existing video colorization techniques operate on a frame-by-frame basis, often overlooking the critical aspect of temporal coherence between successive frames. This approach can result in inconsistencies across frames, leading to undesirable effects like flickering or abrupt color transitions between frames. To address these challenges, we harness the generative capabilities of a fine-tuned latent diffusion model designed specifically for video colorization, introducing a novel solution for achieving temporal consistency in video colorization, as well as demonstrating strong improvements on established image quality metrics compared to other existing methods. Furthermore, we perform a subjective study, where users preferred our approach to the existing state of the art. Our dataset encompasses a combination of conventional datasets and videos from television/movies. In short, by leveraging the power of a fine-tuned latent diffusion-based colorization system with a temporal consistency mechanism, we can improve the performance of automatic video colorization by addressing the challenges of temporal inconsistency. A short demonstration of our results can be seen in some example videos available at https://youtu.be/vDbzsZdFuxM.