StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation
Ke Xing, Longfei Li, Yuyang Yin, Hanwen Liang, Guixun Luo, Chen Fang, Jue Wang, Konstantinos N. Plataniotis
Abstract
The growing adoption of XR devices has fueled strong demand for high-quality stereo video, yet its production remains costly and artifact-prone.To address this challenge, we present **StereoWorld**, an **end-to-end framework** that repurposes a pretrained video generator for high-fidelity monocular-to-stereo video generation. Our framework jointly conditions the model on the monocular video input while explicitly supervising the generation with a **geometry-aware regularization** to ensure 3D structural fidelity.A spatio-temporal tiling scheme is further integrated to enable efficient, high-resolution synthesis.To enable large-scale training and evaluation, we curate a **high-definition stereo video dataset** containing over 11M frames aligned to natural human interpupillary distance (IPD).Extensive experiments demonstrate that StereoWorld substantially outperforms prior methods, generating stereo videos with superior visual fidelity and geometric consistency.
BibTeX
@inproceedings{cvpr2026_stereoworldgeome,
title = {StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation},
author = {Ke Xing and Longfei Li and Yuyang Yin and Hanwen Liang and Guixun Luo and Chen Fang and Jue Wang and Konstantinos N. Plataniotis and Xiaojie Jin and Yao Zhao and Yunchao Wei},
booktitle = {CVPR 2026},
year = {2026}
}