Cinematic-L1 Video Stabilization with a Log-Homography Model

TL;DR

This paper introduces a fast, high-quality video stabilization method using a log-homography model that simulates cinematic camera motions and corrects perspective distortions efficiently.

Contribution

It extends previous work by employing full homographies in log space, incorporating crop and distortion constraints, and developing a real-time, memory-efficient stabilization algorithm.

Findings

Runs at 300fps on an iPhone XS

Produces high-quality stabilized videos

Outperforms previous methods in accuracy and speed

Abstract

We present a method for stabilizing handheld video that simulates the camera motions cinematographers achieve with equipment like tripods, dollies, and Steadicams. We formulate a constrained convex optimization problem minimizing the $\ell_1$-norm of the first three derivatives of the stabilized motion. Our approach extends the work of Grundmann et al. [9] by solving with full homographies (rather than affinities) in order to correct perspective, preserving linearity by working in log-homography space. We also construct crop constraints that preserve field-of-view; model the problem as a quadratic (rather than linear) program to allow for an $\ell_2$ term encouraging fidelity to the original trajectory; and add constraints and objectives to reduce distortion. Furthermore, we propose new methods for handling salient objects via both inclusion constraints and centering objectives. Finally, we describe a windowing strategy to approximate the solution in linear time and bounded memory. Our method is computationally efficient, running at 300fps on an iPhone XS, and yields high-quality results, as we demonstrate with a collection of stabilized videos, quantitative and qualitative comparisons to [9] and other methods, and an ablation study.