StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space
We introduce StereoSpace, a diffusion-based framework for monocular-to-stereo synthesis that models geometry purely through viewpoint conditioning, without explicit depth or warping. A canonical rectified space and the conditioning guide the generator to infer correspondences and fill disocclusions end-to-end. To ensure fair and leakage-free evaluation, we introduce an end-to-end protocol that excludes any ground truth or proxy geometry estimates at test time. The protocol emphasizes metrics reflecting downstream relevance: iSQoE for perceptual comfort and MEt3R for geometric consistency. StereoSpace surpasses other methods from the warp & inpaint, latent-warping, and warped-conditioning categories, achieving sharp parallax and strong robustness on layered and non-Lambertian scenes. This establishes viewpoint-conditioned diffusion as a scalable, depth-free solution for stereo generation.
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Tjark Behrens
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2025-12-15T08:13:49.804Z
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Tjark Behrens
behretj
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behretj
693be7329874a2a5e4ffb447
Anton Obukhov
claimed_verified
2025-12-15T08:13:53.509Z
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Anton Obukhov
toshas
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toshas
693be7329874a2a5e4ffb448
Bingxin Ke
claimed_verified
2025-12-15T08:13:51.708Z
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Bingxin Ke
Bingxin
user
Bingxin
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Fabio Tosi
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Matteo Poggi
693be7329874a2a5e4ffb44b
Konrad Schindler
StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space
We introduce StereoSpace, a diffusion-based framework for monocular-to-stereo synthesis that models geometry purely through viewpoint conditioning, without explicit depth or warping. A canonical rectified space and the conditioning guide the generator to infer correspondences and fill disocclusions end-to-end. To ensure fair and leakage-free evaluation, we introduce an end-to-end protocol that excludes any ground truth or proxy geometry estimates at test time. The protocol emphasizes metrics reflecting downstream relevance: iSQoE for perceptual comfort and MEt3R for geometric consistency. StereoSpace surpasses other methods from the warp & inpaint, latent-warping, and warped-conditioning categories, achieving sharp parallax and strong robustness on layered and non-Lambertian scenes. This establishes viewpoint-conditioned diffusion as a scalable, depth-free solution for stereo generation.
StereoSpace uses viewpoint-conditioned diffusion to generate stereo images without explicit depth or warping, achieving superior performance on various scenes.
diffusion-based framework monocular-to-stereo synthesis viewpoint conditioning canonical rectified space generator correspondences disocclusions end-to-end protocol iSQoE MEt3R perceptual comfort geometric consistency warp & inpaint latent-warping warped-conditioning sharp parallax robustness layered scenes non-Lambertian scenes viewpoint-conditioned diffusion