SplatPainter: Interactive Authoring of 3D Gaussians from 2D Edits via Test-Time Training

The rise of 3D Gaussian Splatting has revolutionized pho-torealistic 3D asset creation, yet a critical gap remains for their inter-active appearance refinement and editing. Existing approaches based ondiffusion or optimization are ill-suited for this task, as they are oftenprohibitively slow, lack the precision for fine-grained control, or—mostimportantly—are fundamentally destructive to the original asset’s iden-tity. To address this, we introduce SplatPainter, a state-aware feedfor-ward model that enables continuous, high-fidelity appearance editing of3D Gaussian assets from user-provided 2D views. Our method directlypredicts updates to the attributes of a compact, feature-rich Gaussianrepresentation and leverages Test-Time Training to create a state-aware,iterative workflow. The versatility of our approach allows a single ar-chitecture to perform diverse tasks, including high-frequency local detailrefinement, local paint-over, and consistent global recoloring, all at inter-active speeds while strictly preserving the asset’s original structure. Ourproject website is at: https://y-zheng18.github.io/SplatPainter/.