The Differentiable Rendering and Implicit Function-based model (DRIFu) draws
its roots from the Pixel-aligned Implicit Function (PIFU), a pioneering 3D
digitization technique initially designed for clothed human bodies. PIFU excels
in capturing nuanced body shape variations within a low-dimensional space and
has been extensively trained on human 3D scans. However, the application of
PIFU to live animals poses significant challenges, primarily due to the
inherent difficulty in obtaining the cooperation of animals for 3D scanning. In
response to this challenge, we introduce the DRIFu model, specifically tailored
for animal digitization. To train DRIFu, we employ a curated set of synthetic
3D animal models, encompassing diverse shapes, sizes, and even accounting for
variations such as baby birds. Our innovative alignment tools play a pivotal
role in mapping these diverse synthetic animal models onto a unified template,
facilitating precise predictions of animal shape and texture. Crucially, our
template alignment strategy establishes a shared shape space, allowing for the
seamless sampling of new animal shapes, posing them realistically, animating
them, and aligning them with real-world data. This groundbreaking approach
revolutionizes our capacity to comprehensively understand and represent avian
forms. For further details and access to the project, the project website can
be found at this https URL

针对动物数字化的挑战，我们介绍了 DRIFu 模型，通过训练一组合成的 3D 动物模型和创新的对齐工具，实现动物形状和纹理的准确预测，并建立共享的形状空间，使得采样、姿势设定、动画和与现实数据对齐成为可能。这一突破性的方法彻底改变了我们全面理解和表征鸟类形态的能力。