F3D-Gaus predicts a pixel-aligned 3D Gaussian representation from a single RGB-D image and uses cycle-aggregative self-supervision plus video-prior refinement to render consistent novel views from monocular training data alone.
Efficient geometry-aware 3d generative adversarial networks
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F3D-Gaus: Feed-forward 3D-aware Generation on ImageNet with Cycle-Aggregative Gaussian Splatting
F3D-Gaus predicts a pixel-aligned 3D Gaussian representation from a single RGB-D image and uses cycle-aggregative self-supervision plus video-prior refinement to render consistent novel views from monocular training data alone.