A recently developed light projection technique can add dynamic impressions to static real objects without changing their original visual attributes such as surface colors and textures. It produces illusory motion impressions in the projection target by projecting gray-scale motion-inducer patterns that selectively drive the motion detectors in the human visual system. However, with this technique, determining the best deformation sizes is often difficult: When users try to add a large deformation, the deviation in the projected patterns from the original surface pattern on the target object becomes apparent. Therefore, to obtain satisfactory results, they have to spend much time and effort to manually adjust the shift sizes. Here, to overcome this limitation, we propose an optimization framework that adaptively retargets the displacement vectors based on a perceptual model. The perceptual model predicts the subjective inconsistency between a projected pattern and an original one by simulating responses in the human visual system. The displacement vectors are adaptively optimized so that the projection effect is maximized within the tolerable range predicted by the model. In the research demonstration, we will present a demo tool that incorporates our optimization technique, where a user can interactively edit dynamic appearances of a real object without cumbersome manual adjustments of deformation sizes.