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Recently, Meitu Image Research Institute (MT Lab) and Beijing Jiaotong University jointly proposed a technique called MEMatte (Memory Efficient Matting), which is a super high-resolution matting technology successfully selected for the top-tier conference in the field of artificial intelligence, AAAI 2025. The main highlight of the MEMatte technology is that it is a memory-friendly natural image matting framework that effectively reduces the computational overhead of the model. This innovation allows for operations in memory-constrained environments, such as commercial graphics cards and edge devices.

CatVTON is a lightweight AI virtual dressing model designed for fashion enthusiasts, with a total parameter count of 899.06M and only 49.57M trainable parameters needed during training. It requires less than 8G of GPU memory for inference and supports 1024x768 high resolution, making it suitable for personal computers. The model can be quickly deployed on ComfyUI and Gradio applications, supporting inference on VITON-HD and DressCode datasets wit....