The rise of 3D Gaussian Splatting has revolutionized photorealistic 3D asset creation, yet a critical gap remains for their interactive refinement and editing. Existing approaches based on diffusion or optimization are ill-suited for this task, as they are often prohibitively slow, destructive to the original asset's identity, or lack the precision for fine-grained control. To address this, we introduce \ourmethod, a state-aware feedforward model that enables continuous editing of 3D Gaussian assets from user-provided 2D view(s). Our method directly predicts updates to the attributes of a compact, feature-rich Gaussian representation and leverages Test-Time Training to create a state-aware, iterative workflow. The versatility of our approach allows a single architecture to perform diverse tasks, including high-fidelity local detail refinement, local paint-over, and consistent global recoloring, all at interactive speeds, paving the way for fluid and intuitive 3D content authoring.
@article{arxiv.2512.05354,
title = {SplatPainter: Interactive Authoring of 3D Gaussians from 2D Edits via Test-Time Training},
author = {Yang Zheng and Hao Tan and Kai Zhang and Peng Wang and Leonidas Guibas and Gordon Wetzstein and Wang Yifan},
journal= {arXiv preprint arXiv:2512.05354},
year = {2025}
}