Animals are diverse in shape, but building a deformable shape model for a new species is not always possible due to the lack of 3D data. We present a method to capture new species using an articulated template and images of that species. In this work, we focus mainly on birds. Although birds represent almost twice the number of species as mammals, no accurate shape model is available. To capture a novel species, we first fit the articulated template to each training sample. By disentangling pose and shape, we learn a shape space that captures variation both among species and within each species from image evidence. We learn models of multiple species from the CUB dataset, and contribute new species-specific and multi-species shape models that are useful for downstream reconstruction tasks. Using a low-dimensional embedding, we show that our learned 3D shape space better reflects the phylogenetic relationships among birds than learned perceptual features.

本文介绍了一种使用关节模板和物种的图像来捕捉新物种的方法，重点关注鸟类。通过解开姿态和形状，从图像证据中学习捕捉各物种和每个物种内部变化的形状空间，并使用低维嵌入，展示了所学3D形状空间比感知特征更好地反映了鸟类之间的系统关系，并提供了一些新的物种特定和多物种形状模型，有利于下游重建任务。

鸟之好：从图像中捕获鸟类形态模型