VitronEdit：跨图像、视频与3D场景的通用视觉编辑
VitronEdit: Universal Visual Editing across Images, Videos, and 3D Scenes
Jing Wang, Yanlin Li, Mong-Li Lee, Wynne Hsu, Shengqiong Wu, Hao Fei
AI 速览：VitronEdit 提出一个统一框架，用共享序列表示和渐进式多模态训练，把图像与视频编辑知识迁移到数据稀缺的3D场景编辑。
Recent advances in unified visual editing have begun to consolidate image and video editing within a single model, while 3D scene editing remains largely separate and constrained by scarce paired training data. Given the rich image and video editing supervision now available and the common editing semantics shared across modalities, a natural question is whether this knowledge can be transferred to data-scarce 3D editing. We present VitronEdit, a universal framework for image, video, and 3D scene editing. VitronEdit adopts a shared sequence representation and progressive multimodal training to transfer editing knowledge across modalities. We further introduce geometry-aware post-training, which leverages a pretrained geometry reconstruction model to provide depth and camera-pose supervision. Surprisingly, applying geometric supervision only to 3D samples yields the best overall performance, while image and video editing also improve despite receiving no direct geometric supervision, suggesting that geometric regularization transfers through the shared backbone. Our analysis further shows that image and video training mainly transfers editing semantics to 3D, whereas 3D supervision primarily improves multi-view consistency. The project page: https://vitron-llm.github.io/VitronEdit/.