{"ID":2832362,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.05354","arxiv_id":"2512.05354","title":"SplatPainter: Interactive Authoring of 3D Gaussians from 2D Edits via Test-Time Training","abstract":"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.","short_abstract":"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 i...","url_abs":"https://arxiv.org/abs/2512.05354","url_pdf":"https://arxiv.org/pdf/2512.05354v1","authors":"[\"Yang Zheng\",\"Hao Tan\",\"Kai Zhang\",\"Peng Wang\",\"Leonidas Guibas\",\"Gordon Wetzstein\",\"Wang Yifan\"]","published":"2025-12-05T01:42:22Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.GR\"]","methods":"[\"Diffusion Model\"]","has_code":false}