← 研踪 PaperTrailCVF · CVPR 2026:cvf:754ae0646d8ab084d0872d2e5e77b59d · 记录 #1
StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation
Xing, KeLi, LongfeiYin, YuyangLiang, HanwenLuo, GuixunFang, ChenWang, JuePlataniotis, Konstantinos N.Jin, XiaojieZhao, YaoWei, Yunchao 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:05
摘要
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.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation
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.
查看此版本解读 →查看此版本原文 ↗