PercHead is a novel approach designed for reconstructing and editing 3D head models from a single image, addressing challenges such as view occlusions and the need for perceptual supervision. The method employs a unified model architecture featuring a dual-branch encoder alongside a Vision Transformer (ViT)-based decoder, which effectively converts 2D image features into a 3D spatial representation. This innovative design marks a significant advancement in the field of single-image 3D head reconstruction, as supported by recent evaluations. By integrating these components, PercHead enhances the accuracy and flexibility of 3D head modeling and editing tasks. The approach has been documented in a recent arXiv publication, situating it within ongoing research efforts to improve perceptual and geometric fidelity in computer vision applications. Its development reflects a broader trend toward leveraging transformer-based architectures for complex spatial understanding from limited visual input. Overall, PercHead contributes meaningfully to the capabilities of AI-driven 3D reconstruction technologies.