DyBluRF: Dynamic Deblurring Neural Radiance Fields for Blurry Monocular Video
(2312.13528)Abstract
Neural Radiance Fields (NeRF), initially developed for static scenes, have inspired many video novel view synthesis techniques. However, the challenge for video view synthesis arises from motion blur, a consequence of object or camera movement during exposure, which hinders the precise synthesis of sharp spatio-temporal views. In response, we propose a novel dynamic deblurring NeRF framework for blurry monocular video, called DyBluRF, consisting of a Base Ray Initialization (BRI) stage and a Motion Decomposition-based Deblurring (MDD) stage. Our DyBluRF is the first that handles the novel view synthesis for blurry monocular video with a novel two-stage framework. In the BRI stage, we coarsely reconstruct dynamic 3D scenes and jointly initialize the base ray, which is further used to predict latent sharp rays, using the inaccurate camera pose information from the given blurry frames. In the MDD stage, we introduce a novel Incremental Latent Sharp-rays Prediction (ILSP) approach for the blurry monocular video frames by decomposing the latent sharp rays into global camera motion and local object motion components. We further propose two loss functions for effective geometry regularization and decomposition of static and dynamic scene components without any mask supervision. Experiments show that DyBluRF outperforms qualitatively and quantitatively the SOTA methods.
Overview
-
DyBluRF is a neural framework designed to produce sharp video frames from blurry monocular footage, overcoming challenges of inaccurate camera poses.
-
It addresses motion blur issues that hinder the creation of sharp, temporally consistent video by deblurring before NeRF optimization.
-
Incorporates an Interleave Ray Refinement Stage for accurate mapping of dynamic scenes and camera pose refinement.
-
Features a Motion Decomposition-based Deblurring Stage with ILSP technique for handling blurriness due to camera and object movement.
-
Validated by the Blurry iPhone Dataset, it outperforms existing methods in structural detail and motion consistency.
Understanding DyBluRF: Enhancing Video Quality with AI
Overview of DyBluRF
Dynamic Deblurring Neural Radiance Fields (DyBluRF) is a revolutionary framework that aims to improve the synthesis of sharp video frames from blurry monocular video footage. Its effectiveness shines through when dealing with inaccurate camera poses commonly found in monocular videos, a scenario where previous state-of-the-art methods struggle.
Challenges in Video Synthesis
Creating immersive video experiences often requires synthesizing video views that are both sharp and temporally consistent. An obstacle to achieving this is motion blur, which occurs when there is movement within a frame during the camera's exposure time. Traditionally, this has been addressed by applying video deblurring techniques prior to using Neural Radiance Fields (NeRF) optimization. However, this approach has limitations, leading to inconsistencies and subpar quality when reconstructing dynamic 3D scenes.
DyBluRF's Solution
DyBluRF introduces two critical stages to address the challenges posed by motion blur:
- Interleave Ray Refinement (IRR) Stage: Here, dynamic scenes are coarsely reconstructed, and the process refines inaccurate camera pose information. This stage ensures that dynamic elements within scenes are accurately mapped, and the issue of imprecise camera poses is addressed by interleave optimization, resulting in a more nuanced and precise reconstruction.
- Motion Decomposition-based Deblurring (MDD) Stage: This innovative stage deals with the blurriness introduced by both camera and object movement. It uses a novel technique—Incremental Latent Sharp-Rays Prediction (ILSP)—which effectively decomposes the motion into global camera movement and local object motion, allowing for a more accurate rendering of sharp spatio-temporal views.
Experimental Validation
DyBluRF was put to the test using a newly synthesized dataset called the Blurry iPhone Dataset, designed to challenge the deblurring algorithms with realistic camera poses. The results demonstrate that DyBluRF outperforms existing methods in terms of both qualitative and quantitative measures. It achieves superior performance by capturing better structural details and more consistent motion rendering.
Implications of DyBluRF
The implications of these advancements are substantial. DyBluRF paves the way for better quality virtual reality content, improved video post-processing, and could serve as a robust tool for filmmakers and video content creators. By dynamically deblurring and synthesizing high-quality videos, the technology introduces new possibilities in realms where immersion and detail are paramount.
Conclusion
DyBluRF marks a significant step forward in video view synthesis. The framework's ability to handle inaccuracies in camera pose and motion blur opens new doors to video enhancement and could potentially transform the field of video processing. This breakthrough not only enhances user experiences but also provides a new baseline for future research within the domain of video quality enhancement.
Create an account to read this summary for free: