Dress-ED is the first large-scale benchmark unifying virtual try-on, try-off, and text-guided garment editing with 146k verified samples plus a multimodal diffusion baseline.
In: CVPR (2023)
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MIRAGE introduces a benchmark for multi-instance image editing and a training-free framework that uses vision-language parsing and parallel regional denoising to achieve precise edits without altering backgrounds.
SpatialEdit provides a benchmark, large synthetic dataset, and baseline model for precise object and camera spatial manipulations in images, with the model beating priors on spatial editing.
CPAM proposes a context-preserving adaptive manipulation method for zero-shot real image editing in diffusion models via preservation adaptation and localized extraction modules, outperforming prior techniques on a new IMBA benchmark.
citing papers explorer
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Dress-ED: Instruction-Guided Editing for Virtual Try-On and Try-Off
Dress-ED is the first large-scale benchmark unifying virtual try-on, try-off, and text-guided garment editing with 146k verified samples plus a multimodal diffusion baseline.
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MIRAGE: Benchmarking and Aligning Multi-Instance Image Editing
MIRAGE introduces a benchmark for multi-instance image editing and a training-free framework that uses vision-language parsing and parallel regional denoising to achieve precise edits without altering backgrounds.
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SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing
SpatialEdit provides a benchmark, large synthetic dataset, and baseline model for precise object and camera spatial manipulations in images, with the model beating priors on spatial editing.
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CPAM: Context-Preserving Adaptive Manipulation for Zero-Shot Real Image Editing
CPAM proposes a context-preserving adaptive manipulation method for zero-shot real image editing in diffusion models via preservation adaptation and localized extraction modules, outperforming prior techniques on a new IMBA benchmark.