QDMouse4M: A Multi-View 3D Mouse Spontaneous Behavior Dataset with Quantum-Dot Markers

Quantitative analysis of animal behavior in neuroscience increasingly relies on accurate 3D pose, yet current large-scale mouse datasets are restricted to top-down views and suffer from occlusion and keypoint ambiguity. We introduce QDMouse4M, a six-view dataset of freely moving mice with physically grounded 2D and 3D keypoint annotations obtained from subdermal quantum-dot fluorescence markers. QDMouse4M contains over four million reflectance frames with matched fluorescence frames, 2D and 3D pose trajectories and behavior classifications. We use the dataset to evaluate markerless 2D and 3D pose estimation with SLEAP and Lightning Pose 3D across training-set sizes, backbones, and in-session/out-of-session splits. We further show that the released trajectories support downstream spontaneous-behavior analysis with Keypoint-MoSeq and stride-level gait measurements. QDMouse4M provides a benchmark-scale, physically grounded resource for developing and evaluating pose estimation, behavior understanding, and biomechanics models under realistic dark-environment laboratory conditions.