脚本感:基于脚本的生成纹理编辑与意图预测
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IEEE transactions on visualization and computer graphics
|November 21, 2025
概括
本研究介绍了ScribbleSense,这是一种使用多式大型语言模型 (MLLMs) 和图像生成进行3D纹理编辑的新方法. 斯克里布尔Sense通过准确地解释用户笔来增强交互式编辑,用于3D资产创建.
科学领域:
- 计算机图形 计算机图形
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 交互式3D模型纹理编辑对于3D资产创建至关重要.
- 目前的方法在利用粗的草稿式互动方面是有限的.
- 涂的抽象性质导致编辑意图模两可,目标位置不清楚.
研究的目的:
- 开发一种有效的基于涂的3D纹理编辑方法.
- 为了应对杂草编辑中含糊不清的意图和不清楚的语义位置的挑战.
- 利用多式大型语言模型 (MLLMs) 来改进交互式编辑.
主要方法:
- 拟议的ScribbleSense方法结合了MLLM和图像生成模型.
- 利用MLLM的视觉能力,从涂中预测编辑意图.
- 采用全球生成的图像来提取本地纹理细节用于语义定.
主要成果:
- ScribbleSense有效地解释了用户涂背后的语义意图.
- 该方法通过局部细节提取来缓解目标语义位置的模两可.
- 在基于交互式涂的纹理编辑中实现了最先进的性能.
结论:
- 斯克里布尔Sense成功地集成了MLLM,用于增强的3D纹理编辑.
- 该方法为基于涂的交互提供了更直观,更有效的方法.
- 展示了交互式3D资产创建工作流程的重大进展.
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