Realiz3D: 3D Generation Made Photorealistic via Domain-Aware Learning
We often aim to generate images that are both photorealistic and 3D-consistent, adhering to precise geometry, material, and viewpoint controls. Typically, this is achieved by fine-tuning an image generator, pre-trained on billions of real images, using renders of synthetic 3D assets, where annotations for control signals are available. While this approach can learn the desired controls, it often compromises the realism of the images due to domain gap between photographs and renders. We observe that this issue largely arises from the model learning an unintended association between the presence of control signals and the synthetic appearance of the images. To address this, we introduce Rea...
| Authors | Ido Sobol, Kihyuk Sohn, Yoav Blum et al. |
|---|---|
| Primary category | cs.GR |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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