One View Is Enough! Monocular Training for In-the-Wild Novel View Generation
Monocular novel-view synthesis has long required multi-view image pairs for supervision, limiting training data scale and diversity. We argue it is not necessary: one view is enough. We present OVIE, trained entirely on unpaired internet images. We leverage a monocular depth estimator as a geometric scaffold at training time: we lift a source image into 3D, apply a sampled camera transformation, and project to obtain a pseudo-target view. To handle disocclusions, we introduce a masked training formulation that restricts geometric, perceptual, and textural losses to valid regions, enabling training on 30 million uncurated images. At inference, OVIE is geometry-free, requiring no depth estimator or 3D representation. Trained exclusively on in-the-wild images, OVIE outperforms prior methods in a zero-shot setting, while being 600x faster than the second-best baseline. Code and models are publicly available at https://github.com/AdrienRR/ovie.
69c38e4695b48f16a300791b
Adrien Ramanana Rahary
claimed_verified
2026-03-26T13:44:23.311Z
660e8e5a5bf5a2bc5c96b30d
/avatars/bf85bb61c86d47f6c7eff424d3e7e766.svg
Ramanana Rahary
AdrienRR
user
AdrienRR
69c38e4695b48f16a300791c
Nicolas Dufour
claimed_verified
2026-03-26T13:43:37.114Z
630652803aed65d34e98eee3
https://cdn-avatars.huggingface.co/v1/production/uploads/630652803aed65d34e98eee3/XG_PuVFA6ziGQZd3UUZSF.jpeg
Nicolas Dufour
nicolas-dufour
user
nicolas-dufour
69c38e4695b48f16a300791d
Patrick Perez
69c38e4695b48f16a300791e
David Picard
One View Is Enough! Monocular Training for In-the-Wild Novel View Generation
Monocular novel-view synthesis has long required multi-view image pairs for supervision, limiting training data scale and diversity. We argue it is not necessary: one view is enough. We present OVIE, trained entirely on unpaired internet images. We leverage a monocular depth estimator as a geometric scaffold at training time: we lift a source image into 3D, apply a sampled camera transformation, and project to obtain a pseudo-target view. To handle disocclusions, we introduce a masked training formulation that restricts geometric, perceptual, and textural losses to valid regions, enabling training on 30 million uncurated images. At inference, OVIE is geometry-free, requiring no depth estimator or 3D representation. Trained exclusively on in-the-wild images, OVIE outperforms prior methods in a zero-shot setting, while being 600x faster than the second-best baseline. Code and models are publicly available at https://github.com/AdrienRR/ovie.
OVIE enables monocular novel-view synthesis from single images using pseudo-target views generated via geometric scaffolding, achieving superior performance with faster inference compared to previous methods.
monocular novel-view synthesis unpaired internet images monocular depth estimator geometric scaffold pseudo-target view masked training formulation disocclusions zero-shot setting