用文字指令控制3D人脸动态编辑，生成逼真连贯虚拟形象
Edit3DGS: Unified Framework for Dynamic Head Editing via 2D Instruction-Guided Diffusion and 3D Gaussian Splatting
- 结合2D文本引导扩散模型与3D高斯点云，实现语义可控的动态编辑
- 支持表情、外貌、外观等细粒度修改，生成无伪影、时序平滑的3D头像
- 适合虚拟人、影视制作、沉浸式通信等需要高质量动态人脸的场景
我们提出Edit3DGS，一个统一的动态3D人脸编辑框架，融合2D文本引导扩散模型与3D高斯点云表示。不同于以往分别处理帧级编辑或静态3D重建的方法，本方法将图像域的语义可控性与逼真的时序一致3D表征相结合。给定输入视频，通过掩码标记可编辑面部区域，并利用文本条件扩散模型实现表达转换、属性修改和外观优化等细粒度操作。编辑后的帧通过3D高斯点云聚合生成连贯、高保真的虚拟形象，同时保留身份特征与运动动态。为保证一致性，框架引入多视角批量编辑与轻量级修复策略，恢复跨时间步丢失的表情信息。实验表明，该框架可实现可控、无伪影的头部编辑，具有流畅的时间过渡，在虚拟人、沉浸式通信、影视制作和互动媒体中具有实际应用价值。
原文摘要 · Abstract (English)
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.
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