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Pixel-aligned Implicit Function (PIFu) effectively captures subtle variationsin body shape within a low-dimensional space through extensive training withhuman 3D scans, its application to live animals presents formidable challengesdue to the difficulty of obtaining animal cooperation for 3D scanning. Toaddress this challenge, we propose the combination of two-stage supervised andself-supervised training to address the challenge of obtaining animalcooperation for 3D scanning. In the first stage, we leverage synthetic animalmodels for supervised learning. This allows the model to learn from a diverseset of virtual animal instances. In the second stage, we use 2D multi-viewconsistency as a self-supervised training method. This further enhances themodel's ability to reconstruct accurate and realistic 3D shape and texture fromlargely available single-view images of real animals. The results of our studydemonstrate that our approach outperforms state-of-the-art methods in bothquantitative and qualitative aspects of bird 3D digitization. The source codeis available at https://github.com/kuangzijian/drifu-for-animals.

Two-stage Synthetic Supervising and Multi-view Consistency Self-supervising based Animal 3D Reconstruction by Single Image