论文标题
3D鸟重建：数据集，模型和形状恢复从单个视图
3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View
论文作者
论文摘要
自动捕获动物姿势正在改变我们研究神经科学和社会行为的方式。运动带有重要的社会提示，但是当前的方法无法及时估计动物的姿势和形状，尤其是对于诸如鸟类等社会动物的姿势和形状，这些动物通常会彼此遮住，环境中的物体。为了解决这个问题，我们首先引入了一种模型和多视图优化方法，我们用来捕获活鸟显示的独特形状和姿势空间。然后，我们引入了一条管道和实验，以进行关键点，掩模，姿势和形状回归，从单一视图中恢复准确的禽姿势。最后，我们提供了广泛的多视图关键点和面具注释，从一组户外鸟舍中的15只社交鸟类收集。可以在https://marcbadger.github.io/avian-mesh上找到带有视频，结果，代码，网格模型和Penn Aviary数据集的项目网站。
Automated capture of animal pose is transforming how we study neuroscience and social behavior. Movements carry important social cues, but current methods are not able to robustly estimate pose and shape of animals, particularly for social animals such as birds, which are often occluded by each other and objects in the environment. To address this problem, we first introduce a model and multi-view optimization approach, which we use to capture the unique shape and pose space displayed by live birds. We then introduce a pipeline and experiments for keypoint, mask, pose, and shape regression that recovers accurate avian postures from single views. Finally, we provide extensive multi-view keypoint and mask annotations collected from a group of 15 social birds housed together in an outdoor aviary. The project website with videos, results, code, mesh model, and the Penn Aviary Dataset can be found at https://marcbadger.github.io/avian-mesh.
