Physically Plausible Pose Refinement using Fully Differentiable Forces
This addresses the challenge of physically plausible pose refinement for hand-object interaction, which is incremental as it builds on existing pose estimation systems.
The paper tackles the problem of refining hand and object pose estimates by modeling the underlying forces between them, achieving improved pose accuracy and contact map matching on the ContactPose dataset without using image data.
All hand-object interaction is controlled by forces that the two bodies exert on each other, but little work has been done in modeling these underlying forces when doing pose and contact estimation from RGB/RGB-D data. Given the pose of the hand and object from any pose estimation system, we propose an end-to-end differentiable model that refines pose estimates by learning the forces experienced by the object at each vertex in its mesh. By matching the learned net force to an estimate of net force based on finite differences of position, this model is able to find forces that accurately describe the movement of the object, while resolving issues like mesh interpenetration and lack of contact. Evaluating on the ContactPose dataset, we show this model successfully corrects poses and finds contact maps that better match the ground truth, despite not using any RGB or depth image data.