  Despite advances in text-to-3D generation methods, generation of multi-object
arrangements remains challenging. Current methods exhibit failures in
generating physically plausible arrangements that respect the provided text
description. We present SceneMotifCoder (SMC), an example-driven framework for
generating 3D object arrangements through visual program learning. SMC
leverages large language models (LLMs) and program synthesis to overcome these
challenges by learning visual programs from example arrangements. These
programs are generalized into compact, editable meta-programs. When combined
with 3D object retrieval and geometry-aware optimization, they can be used to
create object arrangements varying in arrangement structure and contained
objects. Our experiments show that SMC generates high-quality arrangements
using meta-programs learned from few examples. Evaluation results demonstrates
that object arrangements generated by SMC better conform to user-specified text
descriptions and are more physically plausible when compared with
state-of-the-art text-to-3D generation and layout methods.
