VISTA-Brick: Visually Faithful and Physically Stable Brick Program Generation

Reproducing an object's geometry does not guarantee a physically stable assembly. We present VISTA-Brick, a framework for generating visually faithful and physically stable brick programs from multi-view RGB-D observations and text. To support this task, we construct a multimodal extension of StableText2Brick that pairs source-object observations with reference brick programs. VISTA-Brick combines multimodal supervised fine-tuning with online GRPO, using program-level geometric agreement, structural cues, and static-analysis feedback to evaluate the policy's own predictions. During post-training, the physical solver serves as an evaluator rather than repairing sampled programs. We evaluate the learned policy both through direct generation and with optional budgeted physical search. Compared with the supervised baseline, post-training improves program legality and static stability without inference-time correction, while modestly improving geometric fidelity. Under the same physical search procedure and sampling-budget cap, post-trained policies also achieve higher joint geometric–physical success with lower average search cost. These results support learning structural feasibility alongside geometric fidelity, with physical verification complementing rather than substituting for the learned generation capability.