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-            The tracking accuracy of DFM is demonstrated by conditioning the reference dance to a rear leg lifting motion. We compare our method with Fourier Latent Dynamics as a baseline. Due to strong periodic assumptions in both motion representation and reinforcement learning, FLD overly smooths out reference motions. DFM, which relaxes the strong periodic assumption, results in moving up the rear leg by tracking the reference motion details more accurately. 
+            The tracking accuracy of DFM is demonstrated by conditioning the reference dance to a rear leg lifting motion. We compare our method with Fourier Latent Dynamics (FLD) as a baseline. Due to strong periodic assumptions in both motion representation and reinforcement learning, FLD overly smooths out reference motions. DFM, which relaxes the strong periodic assumption, results in moving up the rear leg by tracking the reference motion details more accurately. 
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