DopplerWild: A Doppler Dataset and Benchmark for Human Kinematic Understanding in the Wild

Understanding human kinematics in outdoor, real-world environments is critical for assistive robots and intelligent infrastructure. Many perception systems rely on cameras and LiDAR, which infer motion indirectly from spatial observations, making estimates sensitive to discontinuities and ambiguity under low lighting or at distance. In contrast, mmWave radar is robust to lighting and weather, operates at long range, and directly measures motion through Doppler signatures. However, Doppler-based kinematic understanding remains largely unexplored in the wild because existing datasets are primarily controlled and scripted. We introduce DopplerWild, a dataset and benchmark for evaluating Doppler-based kinematic understanding in the wild. DopplerWild comprises 447k radar frames across four outdoor locations, including an unlabeled subset for self-supervised representation learning and a labeled subset of 539 subjects with nearby-person interference (56\%) and occlusion (16\%). The benchmark spans coarse and fine-grained motion classification, as well as velocity estimation. Beyond aggregate metrics on controlled datasets, DopplerWild evaluation isolates real-world factors: subject variation, location shift, multi-person interference, occlusion, sensing geometry, and low-label regimes. DopplerWild reveals three findings. First, self-supervised pretraining matches supervised performance with less than half the labeled data and improves performance under interference, occlusion, and lateral motion. Second, we identify key challenges for single-view Doppler sensing, including physical limits in lateral motion, subtle asymmetric limb motion, and location distribution shifts. Third, we observe bidirectional transfer asymmetry between DopplerWild and an external controlled Doppler dataset, suggesting that real-world coverage is important for transferable Doppler representations. DopplerWild provides an evaluation framework for characterizing the generalization, limits, and transferability of Doppler-based kinematic representations in the wild.