Rel2Traj: Inter-Source Relation-Aware Trajectory Generation for Text-To-Spatial-Audio

Abstract

"A car drives from front left to front right while a dog follows it." Such a description specifies no particular trajectory, yet imposes a constraint on the joint motion of both sources that must hold at every frame. Existing text-to-spatial-audio methods can position and move each sound source according to its description, but none of them ensures or verifies that such inter-source relations hold. We therefore propose Rel2Traj, which parses the description into sources, relations, and anchors, jointly generates the trajectories of all sources using a relation-conditioned diffusion model, and finally renders sounds from a pretrained text-to-audio model along these trajectories to produce binaural audio. For training and evaluation, we construct a synthetic dataset from a controlled language of source motions and inter-source relations. A single frame-wise relational geometry defines both our evaluation metric, the Relation Satisfaction Rate (RSR), and an auxiliary training loss. On motion-relation combinations held out from training, Rel2Traj maintains all relation types, whereas every baseline fails on at least one. A listening test further confirms that listeners recognize the described relations. To our knowledge, this is the first text-to-spatial-audio work to model relations among moving sources as explicit geometric constraints.