Gaussian-JEPA: Joint-Embedding Predictive Learning for 3D Gaussian Splats
3D Gaussian Splatting (3DGS) represents 3D content with anisotropic primitives that jointly encode geometry and appearance. Fixed-budget encoders consume sampled observations of Gaussian assets, so the same object may be observed through different primitive realizations. Existing self-supervised methods mainly reconstruct masked Gaussian attributes, tying supervision to one sampled realization and requiring an input-space decoder. Latent prediction offers an alternative, but its application to Gaussian tokens requires targets that accommodate coupled attributes and heterogeneous spatial support. We introduce Gaussian-JEPA, which predicts representations of held-out Gaussian token blocks f...
| Authors | Bin Ren, Qi Ma, Yue Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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