  Pixel-aligned Implicit Function (PIFu) effectively captures subtle variations
in body shape within a low-dimensional space through extensive training with
human 3D scans, its application to live animals presents formidable challenges
due to the difficulty of obtaining animal cooperation for 3D scanning. To
address this challenge, we propose the combination of two-stage supervised and
self-supervised training to address the challenge of obtaining animal
cooperation for 3D scanning. In the first stage, we leverage synthetic animal
models for supervised learning. This allows the model to learn from a diverse
set of virtual animal instances. In the second stage, we use 2D multi-view
consistency as a self-supervised training method. This further enhances the
model's ability to reconstruct accurate and realistic 3D shape and texture from
largely available single-view images of real animals. The results of our study
demonstrate that our approach outperforms state-of-the-art methods in both
quantitative and qualitative aspects of bird 3D digitization. The source code
is available at https://github.com/kuangzijian/drifu-for-animals.
