FuncRoom-Agent: Sequential Feed-Forward 3D Functional Indoor Scene Generation
Computer Vision and Pattern Recognition
Summary
The authors introduce a new way to create 3D indoor rooms that focus on the room's function, not just how it looks. They built a special language to describe rooms as step-by-step programs, allowing for detailed object arrangements from big furniture to small items. Instead of slow trial-and-error methods, their system learns to build rooms efficiently by predicting the steps, improving itself with feedback based on how well the room works. They also created a benchmark to test their method, showing it makes more functional and accurate room designs faster than before.
Authors
Hao Feng, Zhi Zuo, MingJian Liang, Jingyu Hu, Xiaowei Hu, Liupengfei Wu, Dian Zhang, Guoxin Fang, Zhengzhe Liu
Abstract
We introduce Function-Room Generation, a new indoor 3D scene generation setting that creates rooms supporting explicit functional goals rather than merely visually plausible layouts. Existing agentic and executable methods improve controllability, but often depend on costly test-time generate--evaluate--revise loops, making functional room generation slow and computationally expensive. We address this challenge with three technical contributions. First, we design a recursive domain-specific language to effectively organize the hierarchical object compositions required by functional rooms, from room structure and major furniture to dense support-surface and nested small objects. It represents rooms as staged executable programs with explicit geometric and functional relations. Second, we propose a sequential feed-forward scene construction framework that distills recursive construction traces into a scene construction expert. At inference time, the expert writes executable DSL code stage by stage, and a deterministic executor directly instantiates each stage without teacher agents, online critics, or iterative repair. Third, we introduce ScenePRM, an execution-grounded process reward framework that improves the expert through reinforcement learning with functional, geometric, relational, and future-constructability feedback. We further establish a function-oriented benchmark and show state-of-the-art performance on both general indoor scene generation and function-room generation, achieving stronger functional completeness, relation correctness, geometric executability, and generation efficiency.