Diagnosing Interaction Effects in Pose Prediction: A Cross-Scenario Frequency-Domain Methodology

Multi-agent pose prediction, the task of forecasting future body poses for multiple people interacting in a shared scene, has become a foundational capability for autonomous systems that share space with people. The standard evaluation methods report a single aggregated error number per model, which cannot separate individual-motion difficulty from the error associated with inter-agent interaction. We address this gap with a three-prong frequency-domain methodology that diagnoses prediction error through complementary spectral decompositions: Fast Fourier Transform, bandpass filtering, and discrete wavelet transform. Applied to both interaction-naive and interaction-aware predictors across different testing scenarios, the three prongs converge on a same pattern: the interaction-associated penalty concentrates below 5 Hz. The results show where interaction stresses pose prediction and suggest multi-agent pose prediction to allocate its interaction-specific modeling capacity in low-to-mid frequencies.