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Mistakes in human demonstrations can enhance robot learning when properly harnessed, revealing a new dimension of value estimation that traditional methods overlook.
By explicitly bridging the gap between on-body appearances and flat layouts, BridgeDiff achieves state-of-the-art virtual try-off results, generating more realistic and structurally sound flat-garment representations.
