发表机构
Universitat Politècnica de Catalunya; Institut de Robòtica i Informàtica Industrial, CSIC-UPC（加泰罗尼亚理工大学; 工业机器人与信息学研究所，CSIC-UPC）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ChromaGS提出一种无需重训练的实时语言引导4D高斯化身颜色编辑方法，通过区域基色与高斯残差分解实现确定性语义控制。
AI 中文摘要
我们提出了ChromaGS，一种用于可动画3D高斯头部化身实时、语言引导颜色编辑的方法。给定一个训练好的可动画化身，用户可以通过自然语言即时修改语义区域的颜色，编辑在渲染时应用，无需重新训练。我们的关键见解是为每个高斯图元增加学习到的语义区域软分配，并将颜色分解为区域级基色和高斯级残差。这种分解实现了连贯的颜色传递：修改区域基色会自然传播到所有相关的高斯，同时保留残差中编码的精细外观细节。一个两阶段语言管道将文本指令转换为目标颜色，支持绝对指定和相对调整。与可能引入意外修改的生成式编辑方法不同，我们的方法提供确定性、精确定位的语义控制。实验证明了在不同主体上的忠实外观保持和直观交互。项目页面和代码可在以下网址获取：this https URL
英文摘要
We present ChromaGS, a method for real-time, language-guided color editing of animatable 3D Gaussian head avatars. Given a trained animatable avatar, users can instantly modify the color of semantic regions through natural language, with edits applied at render time and no retraining required. Our key insight is to augment each Gaussian primitive with learned soft assignments to semantic regions and decompose colors into region-level base colors and Gaussian-level residuals. This decomposition enables coherent color transfer: modifying a region's base color propagates naturally through all associated Gaussians while preserving fine appearance details encoded in residuals. A two-stage language pipeline translates text instructions into target colors, supporting both absolute specifications and relative adjustments. Unlike generative editing methods that may introduce unintended modifications, our approach provides deterministic, precisely localized semantic control. Experiments demonstrate faithful appearance preservation and intuitive interaction across diverse subjects. Project page and code are available at: https://a-canela.github.io/chromags/
CommentsCGIP 2026
