Sparse bundle adjustment (SBA) is the state of the art method for simultaneously optimizing a set of camera poses and 3D points. The multibody bundle adjustment optimizes the static scene and the moving rigid object(s). The result is one camera path representing the main camera motion and virtual camera path(s) for each of the independently moving objects in the scene. The bundle adjustment for the multibody problem is performed in a joint optimization. Main motion (static scene) and object motion(s) are included in the optimization such that the sparse algorithm of SBA can still be applied, even when enforcing the constraint that main camera and object camera share the same intrinsic parameters. The joint optimization approach enables weighting the resulting error for each of the motion models and therefore influences the optimization process. Our experiments with synthetic and natural image data show that an appropriate weighting leads to more accurate camera parameters.