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This work proposes QUALS, a large-scale time series corpus equilibrium framework that significantly enhances data efficiency, enabling existing models to achieve superior performance using only a small fraction of the original training data.
By harnessing rotational symmetry, this method achieves pose estimation accuracy comparable to traditional techniques, even in the absence of 3D models.
