DUT: Learning Video Stabilization by Simply Watching Unstable Videos (2011.14574v3)

Abstract: Previous deep learning-based video stabilizers require a large scale of paired unstable and stable videos for training, which are difficult to collect. Traditional trajectory-based stabilizers, on the other hand, divide the task into several sub-tasks and tackle them subsequently, which are fragile in textureless and occluded regions regarding the usage of hand-crafted features. In this paper, we attempt to tackle the video stabilization problem in a deep unsupervised learning manner, which borrows the divide-and-conquer idea from traditional stabilizers while leveraging the representation power of DNNs to handle the challenges in real-world scenarios. Technically, DUT is composed of a trajectory estimation stage and a trajectory smoothing stage. In the trajectory estimation stage, we first estimate the motion of keypoints, initialize and refine the motion of grids via a novel multi-homography estimation strategy and a motion refinement network, respectively, and get the grid-based trajectories via temporal association. In the trajectory smoothing stage, we devise a novel network to predict dynamic smoothing kernels for trajectory smoothing, which can well adapt to trajectories with different dynamic patterns. We exploit the spatial and temporal coherence of keypoints and grid vertices to formulate the training objectives, resulting in an unsupervised training scheme. Experiment results on public benchmarks show that DUT outperforms state-of-the-art methods both qualitatively and quantitatively. The source code is available at https://github.com/Annbless/DUTCode.

• The paper introduces DUT, a deep unsupervised framework that integrates optical flow-based trajectory estimation with a divide-and-conquer strategy to enhance video stabilization.
• It employs multi-homography and neural network-driven motion refinement to outperform conventional methods in stability and cropping efficiency on benchmark datasets.
• The approach removes the need for paired training data, opening paths for adaptive and integrated video processing solutions in augmented reality and digital film production.

An Expert Review of "DUT: Learning Video Stabilization By Simply Watching Unstable Videos"

The paper "DUT: Learning Video Stabilization By Simply Watching Unstable Videos" presents a novel approach to video stabilization by leveraging deep unsupervised learning. Traditional video stabilizers often employ trajectory estimation and smoothing techniques but rely heavily on hand-crafted features and require paired stable and unstable video data for training. In contrast, this research utilizes the representation capabilities of deep neural networks (DNNs), implementing a divide-and-conquer strategy reminiscent of classical methods to tackle the challenges in real-world scenarios.

Methodology

The DUT framework comprises two main stages: trajectory estimation and trajectory smoothing. During the trajectory estimation phase, the motion of keypoints is first estimated using optical flow, augmented with keypoint detection to ensure robustness against noise, illumination changes, and occlusion. A unique component here is the multi-homography estimation strategy, which aids in handling scenarios with multiple planar motions. Motion refinement is achieved via a neural network, further enhancing the motion accuracy by refining it based on temporally associated grid vertices.

Following estimation, the trajectory smoothing stage deploys a novel network that predicts dynamic smoothing kernels. This innovative approach adapts to diverse dynamic patterns, improving on conventional fixed-kernel methods. The networks are trained in an unsupervised manner by exploiting spatial and temporal coherence, thereby bypassing the need for paired data.

Experimental Validation

The DUT's effectiveness is validated using public benchmarks such as the NUS dataset, demonstrating superior performance over traditional and deep learning-based video stabilizers in terms of stability and distortion. Notably, DUT maintains a competitive cropping ratio, a critical aspect where many stabilization methods often compromise viewer experience to mitigate video shake.

Quantitative and Qualitative Analysis

Quantitative results clearly indicate that DUT outperforms existing state-of-the-art methods, such as Meshflow and Subspace, as well as DNN-based methods like StabNet and DIFRINT, both qualitatively and quantitatively. By addressing the critical need for a robust trajectory estimation strategy, DUT effectively reduces distortion and enhances stability, especially in videos involving complex motions such as parallax and quick rotations. The absence of large displacements during the smoothing phase likely accounts for the compelling cropping results.

Implications and Future Work

The development of DUT suggests a shift towards fully unsupervised stabilization models. Its implications extend beyond stabilization, potentially influencing other domains where video alignment and consistency are pivotal, such as augmented reality, video conferencing, and digital film production. However, while DUT demonstrates considerable promise, challenges such as the need for adaptive parameters and the potential elaboration on fully leveraging neighboring frame data for unstinted stabilization remain open areas for exploration.

Conclusion

Overall, this paper presents a sophisticated, unsupervised video stabilization technique that addresses many limitations of existing models. By integrating trajectory estimation and smoothing within a deep learning context, DUT sets a new standard for efficiency and performance, heralding a new era for video analysis systems. Future research can build on this foundation to create more adaptive and fully integrated stabilization solutions within broader video processing pipelines.