Indirect editing methods for 3D Gaussian Splatting (3DGS) have recently witnessed significant advancements. These approaches operate by first applying edits in the rendered 2D space and subsequently projecting the modifications back into 3D. However, this paradigm inevitably introduces cross-view inconsistencies and constrains both the flexibility and efficiency of the editing process. To address these challenges, we present VF-Editor, which enables native editing of Gaussian primitives by predicting attribute variations in a feedforward manner. To accurately and efficiently estimate these variations, we design a novel variation predictor distilled from 2D editing knowledge. The predictor encodes the input to generate a variation field and employs two learnable, parallel decoding functions to iteratively infer attribute changes for each 3D Gaussian. Thanks to its unified design, VF-Editor can seamlessly distill editing knowledge from diverse 2D editors and strategies into a single predictor, allowing for flexible and effective knowledge transfer into the 3D domain. Extensive experiments on both public and private datasets reveal the inherent limitations of indirect editing pipelines and validate the effectiveness and flexibility of our approach.

翻译：针对三维高斯泼溅（3DGS）的间接编辑方法近年来取得了显著进展。这类方法通常先在渲染的二维空间中进行编辑，再将修改内容投影回三维空间。然而，这种范式不可避免地会引入跨视角不一致性问题，同时限制了编辑过程的灵活性与效率。为解决这些挑战，我们提出了VF-Editor，该方法通过前馈方式预测属性变化，实现了对高斯基元的原生编辑。为准确高效地估计这些变化，我们设计了一种从二维编辑知识中蒸馏得到的新型变分预测器。该预测器对输入进行编码以生成变分场，并采用两个可学习的并行解码函数迭代推断每个三维高斯的属性变化。得益于其统一的设计，VF-Editor能够将来自不同二维编辑器和编辑策略的知识无缝蒸馏至单一预测器中，从而实现向三维领域灵活高效的知识迁移。在公开数据集与私有数据集上的大量实验揭示了间接编辑流程的固有局限性，并验证了本方法的有效性与灵活性。