Real-Time 3D Eye Performance Reconstruction for RGBD Cameras

This paper proposes a real-time method for 3D eye performance reconstruction using a single RGBD sensor. Combined with facial surface tracking, our method generates more pleasing facial performance with vivid eye motions. In our method, a novel scheme is proposed to estimate eyeball motions by minimizing the differences between a rendered eyeball and the recorded image. Our method considers and handles different appearances of human irises, lighting variations and highlights on images via the proposed eyeball model and the L0-based optimization. Robustness and real-time optimization are achieved through the novel 3D Taylor expansion-based linearization. Furthermore, we propose an online bidirectional regression method to handle occlusions and other tracking failures on either of the two eyes from the information of the opposite eye. Experiments demonstrate that our technique achieves robust and accurate eye performance reconstruction for different iris appearances, with various head/face/eye motions, and under different lighting conditions.