# StabStitch++: Unsupervised Online Video Stitching with Spatiotemporal Bidirectional Warps

arXiv: https://arxiv.org/abs/2505.05001 · HTML: https://mlsanity.com/paper/2505.05001/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 71 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Deep Image Homography Estimation](https://mlsanity.com/paper/1606.03798/) (2016)
- [Unsupervised Deep Image Stitching: Reconstructing Stitched Features to Images](https://mlsanity.com/paper/2106.12859/) (2021)
- [Parallax-Tolerant Unsupervised Deep Image Stitching](https://mlsanity.com/paper/2302.08207/) (2023)
- [Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation](https://mlsanity.com/paper/2107.02524/) (2021)
- [Single-Perspective Warps in Natural Image Stitching](https://mlsanity.com/paper/1802.04645/) (2018)
- [Deep Iterative Frame Interpolation for Full-frame Video Stabilization](https://mlsanity.com/paper/1909.02641/) (2019)
- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [DUT: Learning Video Stabilization by Simply Watching Unstable Videos](https://mlsanity.com/paper/2011.14574/) (2020)
- [Deep Rectangling for Image Stitching: A Learning Baseline](https://mlsanity.com/paper/2203.03831/) (2022)
- [Minimum Latency Deep Online Video Stabilization](https://mlsanity.com/paper/2212.02073/) (2022)
- [Video Stitching for Linear Camera Arrays](https://mlsanity.com/paper/1907.13622/) (2019)
- [Real-Time Selfie Video Stabilization](https://mlsanity.com/paper/2009.02007/) (2020)
- [Joint Stabilization and Direction of 360deg Videos](https://mlsanity.com/paper/1901.04161/) (2019)
- [Semi-Supervised Coupled Thin-Plate Spline Model for Rotation Correction and Beyond](https://mlsanity.com/paper/2401.13432/) (2024)
- [Learning Residual Elastic Warps for Image Stitching under Dirichlet Boundary Condition](https://mlsanity.com/paper/2309.01406/) (2023)
- [Eliminating Warping Shakes for Unsupervised Online Video Stitching](https://mlsanity.com/paper/2403.06378/) (2024)

## Built on by (most cited first, 3 of 3)

- [Robust Image Stitching with Optimal Plane](https://mlsanity.com/paper/2508.05903/) (2025)
- [UniStitch: Unifying Semantic and Geometric Features for Image Stitching](https://mlsanity.com/paper/2603.10568/) (2026)
- [Pano360: Perspective to Panoramic Vision with Geometric Consistency](https://mlsanity.com/paper/2603.12013/) (2026)

## Abstract (arXiv)

We retarget video stitching to an emerging issue, named warping shake, which unveils the temporal content shakes induced by sequentially unsmooth warps when extending image stitching to video stitching. Even if the input videos are stable, the stitched video can inevitably cause undesired warping shakes and affect the visual experience. To address this issue, we propose StabStitch++, a novel video stitching framework to realize spatial stitching and temporal stabilization with unsupervised learning simultaneously. First, different from existing learning-based image stitching solutions that typically warp one image to align with another, we suppose a virtual midplane between original image planes and project them onto it. Concretely, we design a differentiable bidirectional decomposition module to disentangle the homography transformation and incorporate it into our spatial warp, evenly spreading alignment burdens and projective distortions across two views. Then, inspired by camera paths in video stabilization, we derive the mathematical expression of stitching trajectories in video stitching by elaborately integrating spatial and temporal warps. Finally, a warp smoothing model is presented to produce stable stitched videos with a hybrid loss to simultaneously encourage content alignment, trajectory smoothness, and online collaboration. Compared with StabStitch that sacrifices alignment for stabilization, StabStitch++ makes no compromise and optimizes both of them simultaneously, especially in the online mode. To establish an evaluation benchmark and train the learning framework, we build a video stitching dataset with a rich diversity in camera motions and scenes. Experiments exhibit that StabStitch++ surpasses current solutions in stitching performance, robustness, and efficiency, offering compelling advancements in this field by building a real-time online video stitching system.