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. The project webpage is available at https://ke-xing.github.io/StereoWorld/.
693a379e74fced5bf9c32412
Ke Xing
claimed_verified
2025-12-11T10:13:26.656Z
6486ff6561053da6442fef1a
https://cdn-avatars.huggingface.co/v1/production/uploads/6486ff6561053da6442fef1a/72sdWErAwWtWNJIV5VZsy.jpeg
KeXing
KXingLab
user
KXingLab
693a379e74fced5bf9c32413
Longfei Li
693a379e74fced5bf9c32414
Yuyang Yin
admin_assigned
2025-12-11T10:43:30.256Z
64b7ab4c037d6452a31910eb
https://cdn-avatars.huggingface.co/v1/production/uploads/64b7ab4c037d6452a31910eb/0UaBtwyQTysBMndFWZdKu.png
yuyangyin
yuyangyin
user
yuyangyin
693a379e74fced5bf9c32415
Hanwen Liang
693a379e74fced5bf9c32416
Guixun Luo
693a379e74fced5bf9c32417
Chen Fang
693a379e74fced5bf9c32418
Jue Wang
693a379e74fced5bf9c32419
Konstantinos N. Plataniotis
693a379e74fced5bf9c3241a
Xiaojie Jin
693a379e74fced5bf9c3241b
Yao Zhao
693a379e74fced5bf9c3241c
Yunchao Wei
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. The project webpage is available at https://ke-xing.github.io/StereoWorld/.
StereoWorld generates high-quality stereo video from monocular input using a pretrained video generator with geometry-aware regularization and spatio-temporal tiling.
stereo video monocular-to-stereo pretrained video generator geometry-aware regularization spatio-temporal tiling high-definition stereo video dataset natural human interpupillary distance (IPD) visual fidelity geometric consistency