Edit3DGS：通过2D指令引导扩散与3D高斯泼溅的动态头部编辑统一框架
Edit3DGS: Unified Framework for Dynamic Head Editing via 2D Instruction-Guided Diffusion and 3D Gaussian Splatting
AI总结：
提出Edit3DGS统一框架，结合2D指令引导扩散与3D高斯泼溅，实现动态3D头部的可控编辑，支持表情变换、属性修改等操作，并保持身份与运动动态的一致性。
摘要：
我们提出Edit3DGS，一个用于动态3D头部编辑的统一框架，它将2D指令引导扩散与3D高斯泼溅相结合。与先前分别处理基于帧的编辑或静态3D重建的方法不同，我们的方法将图像域中的语义可控性与逼真、时间一致的3D表示结合起来。给定输入视频，可编辑的面部区域被掩码并使用文本条件扩散模型进行修改，以支持细粒度操作，如表情变换、属性修改和外观细化。然后，编辑后的帧通过3D高斯泼溅聚合，生成一个连贯、高保真的化身，同时保留身份和运动动态。为了强制一致性，Edit3DGS采用了多视图批量编辑和轻量级修复策略，以恢复跨时间步丢失的表情。实验结果表明，我们的框架能够实现可控、无伪影的头部编辑，并具有平滑的时间过渡，在虚拟化身、沉浸式通信、电影制作和交互媒体中具有实际应用。
英文摘要：
We present Edit3DGS, a unified framework for dynamic 3D head editing that integrates 2D instruction-guided diffusion with 3D Gaussian splatting. Unlike prior approaches that separately address frame-based edits or static 3D reconstruction, our method couples semantic controllability in the image domain with photorealistic, temporally consistent 3D representations. Given an input video, editable facial regions are masked and modified using a text-conditioned diffusion model to support fine-grained operations such as expression transformation, attribute modification, and appearance refinement. The edited frames are then aggregated through 3D Gaussian splatting to produce a coherent, high-fidelity avatar that preserves both identity and motion dynamics. To enforce consistency, Edit3DGS incorporates multi-view batch editing and lightweight inpainting strategies that recover lost expressions across timesteps. Experimental results demonstrate that our framework enables controllable, artifact-free head editing with smooth temporal transitions, offering practical applications in virtual avatars, immersive communication, film production, and interactive media.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
