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Existing 3D-from-2D generators are typically designed for well-curated single-category datasets, where all the objects have (approximately) the same scale, 3D location, and orientation, and the camera always points to the center of the scene.
Multiple view geometry in computer vision
Richard Hartley and Andrew Zisserman · 2003
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Pointnet/pointnet++ pytorch
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3d photography using context-aware layered depth inpainting
3d-aware image synthesis via learning structural and textural representations
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Virtual normal: Enforcing geometric constraints for accurate and robust depth prediction
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Sherwin Bahmani, Jeong Joon Park, Despoina Paschalidou, Hao Tang, Gordon Wetzstein, Leonidas Guibas, Luc Van Gool, and Radu Timofte · 2022
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Efficient geometry-aware 3D generative adversarial networks
Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein · 2022
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Unconstrained scene generation with locally conditioned radiance fields
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Diffusion models beat gans on image synthesis
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Clip-nerf: Text-and-image driven manipulation of neural radiance fields
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Multi-view consistent generative adversarial networks for 3d-aware image synthesis
Xuanmeng Zhang, Zhedong Zheng, Daiheng Gao, Bang Zhang, Pan Pan, and Yi Yang · 2022
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Generative multiplane images: Making a 2d gan 3d-aware
Xiaoming Zhao, Fangchang Ma, David Güera, Zhile Ren, Alexander G. Schwing, and Alex Colburn · 2022
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