将3D生成模型重新利用于自回归布局生成
Repurposing 3D Generative Model for Autoregressive Layout Generation
AI总结：
本文提出LaviGen框架，利用3D生成模型直接在3D空间中生成布局，通过自回归过程建模几何关系和物理约束，提升3D场景的物理合理性与效率。
摘要：
我们介绍LaviGen，一种将3D生成模型重新用于3D布局生成的框架。与以往基于文本描述推断物体布局的方法不同，LaviGen直接在原生3D空间中操作，将布局生成作为自回归过程，显式建模物体间的几何关系和物理约束，生成连贯且物理合理的3D场景。为进一步提升此过程，我们提出一种适应的3D扩散模型，整合场景、物体和指令信息，并采用双引导自滚动蒸馏机制以提高效率和空间准确性。在LayoutVLM基准上的大量实验表明，LaviGen在3D布局生成性能上优于现有方法，其物理合理性比最先进方法高19%，计算速度快65%。我们的代码在https://github.com/fenghora/LaviGen公开可用。
英文摘要：
We introduce LaviGen, a framework that repurposes 3D generative models for 3D layout generation. Unlike previous methods that infer object layouts from textual descriptions, LaviGen operates directly in the native 3D space, formulating layout generation as an autoregressive process that explicitly models geometric relations and physical constraints among objects, producing coherent and physically plausible 3D scenes. To further enhance this process, we propose an adapted 3D diffusion model that integrates scene, object, and instruction information and employs a dual-guidance self-rollout distillation mechanism to improve efficiency and spatial accuracy. Extensive experiments on the LayoutVLM benchmark show LaviGen achieves superior 3D layout generation performance, with 19% higher physical plausibility than the state of the art and 65% faster computation. Our code is publicly available at https://github.com/fenghora/LaviGen.
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。
