Dreamcrafter: 通过灵活、生成性输入和输出实现沉浸式3D辐射场编辑
Dreamcrafter: Immersive Editing of 3D Radiance Fields Through Flexible, Generative Inputs and Outputs
AI总结：
Dreamcrafter通过灵活的生成式输入和输出，实现实时沉浸式3D辐射场编辑，结合模块化架构和多层级控制提升创作效率与沉浸感。
摘要：
3D场景创作是空间计算应用中的核心任务。降低现有障碍的竞争性观点包括（1）专注于沉浸式、直接的3D内容操作，或（2）利用AI技术捕捉真实场景（如NeRFs、3D高斯点撒技术等3D辐射场）并在更高抽象层次上修改它们，但代价是高延迟。我们统一了这些方法的互补优势，并研究如何将生成式AI的进步整合到实时、沉浸式的3D辐射场编辑中。我们介绍了Dreamcrafter，一种基于VR的3D场景编辑系统，该系统：（1）提供模块化架构以整合生成式AI算法；（2）结合不同层次的控制来创建对象，包括自然语言和直接操作；（3）引入代理表示，以支持在高延迟操作期间的交互。我们提供了关于控制偏好的实证发现，并讨论了生成式AI接口超越文本输入如何增强场景编辑和世界构建的创造力。
英文摘要：
Authoring 3D scenes is a central task for spatial computing applications. Competing visions for lowering existing barriers are (1) focus on immersive, direct manipulation of 3D content or (2) leverage AI techniques that capture real scenes (3D Radiance Fields such as, NeRFs, 3D Gaussian Splatting) and modify them at a higher level of abstraction, at the cost of high latency. We unify the complementary strengths of these approaches and investigate how to integrate generative AI advances into real-time, immersive 3D Radiance Field editing. We introduce Dreamcrafter, a VR-based 3D scene editing system that: (1) provides a modular architecture to integrate generative AI algorithms; (2) combines different levels of control for creating objects, including natural language and direct manipulation; and (3) introduces proxy representations that support interaction during high-latency operations. We contribute empirical findings on control preferences and discuss how generative AI interfaces beyond text input enhance creativity in scene editing and world building.
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。
