Title: DecompDreamer: A Composition-Aware Curriculum for Structured 3D Asset Generation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: text-to-3d generation, gaussian splatting, compositional 3d generation

Abstract: Current text-to-3D methods excel at generating single objects but falter on compositional prompts. We argue this failure is fundamental to their optimization schedules, as simultaneous or iterative heuristics predictably collapse under a combinatorial explosion of conflicting gradients, leading to entangled geometry or catastrophic divergence. In this paper, we reframe the core challenge of compositional generation as one of optimization scheduling. We introduce DecompDreamer, a framework built on a novel staged optimization strategy that functions as an implicit curriculum. Our method first establishes a coherent structural scaffold by prioritizing inter-object relationships before shifting to the high-fidelity refinement of individual components. This temporal decoupling of competing objectives provides a robust solution to gradient conflict. Qualitative and quantitative evaluations on diverse compositional prompts demonstrate that DecompDreamer outperforms state-of-the-art methods in fidelity, disentanglement, and spatial coherence.

TL;DR: We introduce DecompDreamer, a Gaussian-splatting-based framework for generating high-quality compositional 3D assets from text.