Interactive animation systems often use a level-of-detail (LOD) control to reduce the computational cost by eliminating unperceivable details of the scene. Most methods employ a multiresolutional representation of animation and geometrical data, and adaptively change the accuracy level according to the importance of each character. Multilinear analysis provides the efficient representation of multidimensional and multimodal data, including human motion data, based on statistical data correlations. This paper proposes a LOD control method of motion synthesis with a multilinear model. Our method first extracts a small number of principal components of motion samples by analyzing three-mode correlations among joints, time, and samples using high-order singular value decomposition. A new motion is synthesized by interpolating the reduced components using geostatistics, where the prediction accuracy of the resulting motion is controlled by adaptively decreasing the data dimensionality. We introduce a hybrid algorithm to optimize the reduction size and computational time according to the distance from the camera while maintaining visual quality. Our method provides a practical tool for creating an interactive animation of many characters while ensuring accurate and flexible controls at a modest level of computational cost.