Learning 3D Object Spatial Relationships from Pre-trained 2D Diffusion Models (2503.19914v1)

Abstract: We present a method for learning 3D spatial relationships between object pairs, referred to as object-object spatial relationships (OOR), by leveraging synthetically generated 3D samples from pre-trained 2D diffusion models. We hypothesize that images synthesized by 2D diffusion models inherently capture plausible and realistic OOR cues, enabling efficient ways to collect a 3D dataset to learn OOR for various unbounded object categories. Our approach begins by synthesizing diverse images that capture plausible OOR cues, which we then uplift into 3D samples. Leveraging our diverse collection of plausible 3D samples for the object pairs, we train a score-based OOR diffusion model to learn the distribution of their relative spatial relationships. Additionally, we extend our pairwise OOR to multi-object OOR by enforcing consistency across pairwise relations and preventing object collisions. Extensive experiments demonstrate the robustness of our method across various object-object spatial relationships, along with its applicability to real-world 3D scene arrangement tasks using the OOR diffusion model.