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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.
By fusing sparse coding with visual Transformers, LISTA-Transformer achieves state-of-the-art fault diagnosis accuracy, surpassing traditional methods by 3.3% on the CWRU dataset.
