RelArt: Relation-Aware Articulated Object Synthesis from Executable Structural Priors

Existing methods for articulated object generation often predict joint structure and part geometry together from limited observations. The observations may not determine hidden joint axes or the space needed for parts to move, and errors in the predicted structure can cause disconnected parts or collisions. We therefore use an executable structural prior to specify the articulation structure instead of inferring it from the observation alone. We study geometry generation conditioned on this prior, which specifies parts, joints, interfaces, and motion constraints. Our framework, RelArt, includes an agent-based module for constructing these priors and an autoregressive model for generating part geometry. The construction module derives relations between parts from template code and a small set of valid parameter instances. It executes each proposed instance, checks the relations, and repairs parameters that affect failed checks. The verified relations also form the graph supplied to the geometry model. When generating a part, the decoder retrieves its relations to previously generated neighbors and pairs each neighbor's relations with that neighbor's generated geometry. This gives the decoder both the required connection and the geometry to which the new part must fit. With the same image and structural prior, RelArt improves F-score from 0.733 to 0.788 and motion success from 0.781 to 0.869 over the strongest existing generator in our comparison. In a separate construction experiment with equal proposal and executor-call budgets, repairing parameters that affect failed checks produces more valid articulated instances and covers more templates than resampling all parameters.