Rat behavior modeling goes to the heart of many scientific studies, yet the textureless body surface evades automatic analysis as it literally has no keypoints that detectors can find. The movement of the body surface, however, is a rich source of information for deciphering the rat behavior. We introduce two key contributions to automatically recover densely 3D sampled rat body surface points, passively. The first is RatDome, a novel multi-camera system for rat behavior capture, and a large-scale dataset captured with it that consists of pairs of 3D keypoints and 3D body surface points. The second is RatBodyFormer, a novel network to transform detected keypoints to 3D body surface points. RatBodyFormer is agnostic to the exact locations of the 3D body surface points in the training data and is trained with masked-learning. We experimentally validate our framework with a number of real-world experiments. Our results collectively serve as a novel foundation for automated rat behavior analysis and will likely have far-reaching implications for biomedical and neuroscientific research.

翻译：大鼠行为建模是众多科学研究的核心，然而无纹理的体表因其缺乏检测器可识别的关键点，长期难以实现自动化分析。实际上，体表运动是解析大鼠行为的重要信息源。本文提出两项关键贡献，以被动方式自动重建密集三维采样的鼠体表面点云。首先是RatDome——一种新型多视角大鼠行为捕捉系统，以及利用该系统采集的大规模数据集，其中包含成对的三维关键点与三维体表点。其次是RatBodyFormer——一种将检测到的关键点转换为三维体表点云的新型网络。该网络对训练数据中三维体表点的精确空间分布具有不变性，并采用掩码学习进行训练。我们通过系列真实场景实验验证了该框架的有效性。研究成果共同构成了自动化大鼠行为分析的新基础，预计将对生物医学与神经科学研究产生深远影响。