ArticFlow: Generative Simulation of Articulated Mechanisms
This work addresses the problem of generating articulated 3D shapes for robotics and simulation, representing an incremental improvement over static shape generation methods.
The paper tackles the challenge of generating articulated 3D mechanisms by introducing ArticFlow, a two-stage flow matching framework that learns a controllable velocity field for action-dependent deformations, achieving higher kinematic accuracy and better shape quality compared to existing methods.
Recent advances in generative models have produced strong results for static 3D shapes, whereas articulated 3D generation remains challenging due to action-dependent deformations and limited datasets. We introduce ArticFlow, a two-stage flow matching framework that learns a controllable velocity field from noise to target point sets under explicit action control. ArticFlow couples (i) a latent flow that transports noise to a shape-prior code and (ii) a point flow that transports points conditioned on the action and the shape prior, enabling a single model to represent diverse articulated categories and generalize across actions. On MuJoCo Menagerie, ArticFlow functions both as a generative model and as a neural simulator: it predicts action-conditioned kinematics from a compact prior and synthesizes novel morphologies via latent interpolation. Compared with object-specific simulators and an action-conditioned variant of static point-cloud generators, ArticFlow achieves higher kinematic accuracy and better shape quality. Results show that action-conditioned flow matching is a practical route to controllable and high-quality articulated mechanism generation.