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FlexiEdit: Frequency-Aware Latent Refinement for Enhanced Non-Rigid Editing

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arxiv 2407.17850 v1 pith:UK6B6JCB submitted 2024-07-25 cs.CV

classification cs.CV
keywords imagelatentddimeditinglayoutcomponentseditsflexiedit
verification ladder T0 review T1 audit T2 compute T3 formal
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Current image editing methods primarily utilize DDIM Inversion, employing a two-branch diffusion approach to preserve the attributes and layout of the original image. However, these methods encounter challenges with non-rigid edits, which involve altering the image's layout or structure. Our comprehensive analysis reveals that the high-frequency components of DDIM latent, crucial for retaining the original image's key features and layout, significantly contribute to these limitations. Addressing this, we introduce FlexiEdit, which enhances fidelity to input text prompts by refining DDIM latent, by reducing high-frequency components in targeted editing areas. FlexiEdit comprises two key components: (1) Latent Refinement, which modifies DDIM latent to better accommodate layout adjustments, and (2) Edit Fidelity Enhancement via Re-inversion, aimed at ensuring the edits more accurately reflect the input text prompts. Our approach represents notable progress in image editing, particularly in performing complex non-rigid edits, showcasing its enhanced capability through comparative experiments.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Q-Align: Alleviating Attention Leakage in Zero-Shot Appearance Transfer via Query-Query Alignment

    cs.CV 2025-08 conditional novelty 6.0 of 10

    Q-Align aligns queries between two images and rearranges keys and values to suppress attention leakage in zero-shot appearance transfer.

  2. FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields

    cs.GR 2025-07 conditional novelty 5.0 of 10

    FlowDrag combines 3D mesh deformation with diffusion-based drag editing, using the resulting 2D vector flow to steer the denoising process, and adds a ground-truth benchmark built from video frames.

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