Markerless motion capture addresses key barriers limiting the clinical uptake of biomechanical assessments by enabling efficient data collection and standardized modeling, making it well-suited for multicentre research. This study assessed whether gait deviations associated with knee osteoarthritis (OA) could be consistently detected using markerless motion capture across three clinical centres in Canada. Gait data from 486 participants (351 with knee OA;​ 135 controls) were analyzed, with body segment kinematics estimated from video using Theia3D. Principal component analysis and linear models were used to evaluate joint kinematics and temporal-distance parameters across groups and sites. After pooling data across centres, individuals with knee OA exhibited characteristic gait deviations, including slower walking speed, reduced hip, knee, and ankle range of motion, and increased knee adduction, compared to controls. These deviations were observed consistently across all three centres. Inter-site differences in joint kinematics were minor (RMS < 3°), remained within reported inter-site error thresholds from marker-based systems, and did not obscure group-level effects. These findings demonstrate that clinically meaningful gait deviations can be reliably detected using markerless motion capture in varied clinical environments without extensive standardization. This work supports its use in multicentre studies and highlights its potential to enable large-scale biomechanical research, an essential step toward broader clinical integration of movement analysis.