TokenGS: Decoupling 3D Gaussian Prediction from Pixels with Learnable Tokens
In this work, we revisit several key design choices of modern Transformer-based approaches for feed-forward 3D Gaussian Splatting (3DGS) prediction. We argue that the common practice of regressing Gaussian means as depths along camera rays is suboptimal, and instead propose to directly regress 3D mean coordinates using only a self-supervised rendering loss. This formulation allows us to move from the standard encoder-only design to an encoder-decoder architecture with learnable Gaussian tokens, thereby unbinding the number of predicted primitives from input image resolution and number of views. Our resulting method, TokenGS, demonstrates improved robustness to pose noise and multiview inc...
| Authors | Jiawei Ren, Michal Jan Tyszkiewicz, Jiahui Huang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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