UCLA

This thesis presents a novel, biomimetic model of the eye for realistic virtual human anima- tion. We also introduce a deep learning approach to oculomotor control that is compatible with our biomechanical eye model. Our eye model consists of the following functional com- ponents: (i) submodels of the 6 extraocular muscles that actuate realistic eye movements, (ii) an iris submodel, actuated by pupillary muscles, that accommodates to incoming light intensity, (iii) a corneal submodel and a deformable, ciliary-muscle-actuated lens submodel, which refract incoming light rays for focal accommodation, and (iv) a retina with a multi- tude of photoreceptors arranged in a biomimetic, foveated distribution. The light intensity captured by the photoreceptors is computed using ray tracing from photoreceptor positions through the finite aperture pupil into the 3D virtual environment, and the visual infor- mation is output by the eye via an optic nerve vector. Our oculomotor control system includes a neuromuscular motor controller implemented as a locally-connected, irregular Deep Neural Network (DNN) that conforms to the irregular retinal photoreceptor distribu- tion, plus auxiliary Shallow Neural Networks (SNNs) that control the accommodation of the pupil and lens. The neuromuscular controller is trained offline through deep learning from visual data synthesized by the eye model itself. Once trained, it operates robustly and efficiently online, innervating the extraocular muscles to produce natural eye movements in order to foveate and pursue moving visual targets. We demonstrate the operation of our eye model binocularly within a recently introduced sensorimotor control framework involving an anatomically-accurate biomechanical human musculoskeletal model.