Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation
Paper • 2607.05354 • Published
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DAVIS GRT Evaluation Masks
Precomputed GRT evaluation masks for DAVIS videos, released for Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation (ICML 2026).
- Source videos: DAVIS 2017 480p
- Depth model: Video Depth Anything Large
- Relative stereo shift scale:
0.06 * video_width - Format: compressed
.npz, one mask sequence per video - Code: github.com/Visual-AI/GRT
- Project page: visual-ai.github.io/grt
- Paper: arXiv:2607.05354
Masks preserve the source-video filename stem: <video_stem>.mp4 matches <video_stem>.npz.
Citation
@inproceedings{lu2026grt,
author = {Jingyi Lu and Kai Han},
title = {Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2026},
}
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