The current occlusion-aware blending module may have limitations in handling more challenging cases where complex interactions between different head components occur. One potential limitation could be the accuracy of depth estimation and occlusion detection, especially in scenarios with intricate geometries or overlapping structures. To improve the module, advanced depth sensing techniques, such as depth from defocus or structured light, could be integrated to enhance the precision of depth maps. Additionally, incorporating machine learning algorithms for more robust occlusion detection and blending strategies could help address challenging cases where occlusions are ambiguous or dynamic. By refining the occlusion-aware blending module with advanced depth sensing and intelligent algorithms, the system can better handle complex scenarios and produce more realistic renderings.