← 研踪 PaperTrailCVF · CVPR 2026:cvf:d28087206b9e9d3359bc5fb5adff0116 · 记录 #1
Repurposing 3D Generative Model for Autoregressive Layout Generation
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 23:17
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 to integrate scene, object, and instruction information and employ 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. We will release our code publicly.
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记录 #1 · 2026 · 最新观测版本
Repurposing 3D Generative Model for Autoregressive Layout Generation
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 to integrate scene, object, and instruction information and employ 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. We will release our code publicly.
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