Can a neural network learn to render 3D Gaussian splats on its own, with no rasterizer in the renderer? We present GaussianRenderFormer, a transformer that takes a set of Gaussians and a camera and ou
Can a neural network learn to render 3D Gaussian splats on its own, with no rasterizer in the renderer? We present GaussianRenderFormer, a transformer that takes a set of Gaussians and a camera and outputs the image directly. Each Gaussian becomes one token in a view-independent encoder, and a view-dependent decoder turns small patches of camera rays into pixels by attending to those tokens. Trained on 26,820 Objaverse objects pruned to 20,000 Gaussians each, GaussianRenderFormer renders 300 unseen objects at 38.08 dB PSNR against the full, unpruned splats, while rasterizing the same pruned input reaches 44.02 dB. The gap is not one of generalization, and our diagnostics are consistent…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
GaussianRenderFormer: Transformer-based Rendering of 3D Gaussian Splats was first seen on Hugging Face · Daily papers on Nov 5, 2026. Since then it has appeared in 1 check on 1 different day. Its best position was #30 of 30, reached on Nov 5, 15:28 UTC. It dropped off the list after Nov 5, 15:28 UTC.
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It was first seen on Nov 5, 2026 and has been on the list on 1 day (1 check) since.