Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation
Monocular-to-stereo conversion synthesizes stereoscopic content from 2D videos for immersive 3D experiences. In modern Depth-Image-Based Rendering (DIBR) approaches, stereo inpainting of disocclusions is the critical bottleneck. Training-based methods achieve superior quality but rely on scarce stereo pairs or synthetic data with domain gaps. We address this through the first self-supervised framework learning from monocular videos via cycle consistency. Our key contribution is the Geometric Reciprocity Theorem (GRT): under the nearest-neighbor DIBR formulation, the disocclusion mask when synt
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- LinkedLinked via arxiv author · 85%Jingyi Lu →
“Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation”
- LinkedLinked via arxiv author · 85%Kai Han →
“Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation”