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This paper introduces VIP-SAM for instance-level garment segmentation and CtrlVTON, a controllable virtual try-on framework that treats try-on as an image editing problem, allowing precise control over garment layout, style, and placement. Both methods achieve state-of-the-art results on their respective tasks.
ControlLight is a controllable low-light enhancement framework that uses a large-scale real-world dataset and a weighted flow matching loss to achieve consistent image quality across varying enhancement strengths, achieving state-of-the-art performance.