Edit3r: Instant 3D Scene Editing from Sparse Unposed Images

We present Edit3r , a feed-forward framework that recon-structs and edits a 3D scene in a single pass. Unlike prior approachesthat require per-scene optimization, Edit3r directly predicts instruction-aligned 3D Gaussians from unposed sparse-view images and a text prompt.A key challenge in training 3D editing models is the lack of view-consistentedited images for supervision, due to the stochastic nature of 2D imageediting. We address this by reformulating editing as a recoloring pretexttask, and show that a model trained on simple recoloring transfers effec-tively to 3D-consistent editing at inference time. Specifically, we develop(i) a SAM2-based recoloring pipeline that generates one recolored ref-erence view along with reliable, cross-view-consistent supervision views,and (ii) an asymmetric input strategy that pairs the recolored refer-ence view with raw auxiliary views, encouraging the network to fuse andalign disparate observations. At inference, it supports diverse 2D editorsby resolving cross-view conflicts and yielding coherent 3D edits, withoutrequiring multi-view edited supervision during training. For large-scalequantitative evaluation, we introduce DL3DV-Edit-Bench. Compre-hensive quantitative and qualitative results show that Edit3r achievessuperior semantic alignment and enhanced 3D consistency compared torecent baselines, while operating at significantly higher inference speed,making it promising for real-time 3D editing applications.