In this paper, we propose an efficient method for robust 3D self-portraits using a single RGBD camera. Benefiting from the proposed PIFusion and lightweight bundle adjustment algorithm, our method can generate detailed 3D self-portraits in seconds and shows the ability to handle extremely loose clothes. To achieve highly efficient and robust reconstruction, we contribute PIFusion, which combines learning-based 3D recovery with volumetric non-rigid fusion to generate accurate sparse partial scans of the performer. Moreover, a non-rigid volumetric deformation method is proposed to continuously refine the learned shape prior. Finally, a lightweight bundle adjustment algorithm is proposed to guarantee that all the partial scans can not only ``loop'' with each other, but also keep consistent with the selected live key observations. Results and experiments show that the proposed method achieves more robust and efficient 3D self-portraits compared with state-of-the-art methods.

Fig 1. Overview of our method.

Fig 2. System setup and live demo.

Fig 3. Example 3D portraits acquired by our system.