AIM-Bench is the first dedicated benchmark for editing images to evoke specific emotions with fine-grained control, paired with AIM-40k dataset that delivers a 9.15% performance gain by correcting training data imbalances.
Null-text Inversion for Editing Real Images using Guided Diffusion Models
6 Pith papers cite this work, alongside 16 external citations. Polarity classification is still indexing.
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DRFS is a new inversion-free editing technique for rectified flow models that models source-target velocity discrepancies and applies a time-dependent shift to improve fidelity and unify prior methods like DDS and FlowEdit.
NeoMap introduces a training-free framework using convergent manifold alternating projection iterations to extract high-fidelity novel views from pre-trained video models, outperforming prior methods on standard benchmarks.
Zero-shot inversion-free flow method de-identifies skin images in under 20 seconds while preserving pathological features with IoU stability exceeding 0.67 using segment-by-synthesis and CIELAB decoupling.
SketchDeco performs training-free sketch colourisation via diffusion inversion to insert user colors followed by custom self-attention blending for local fidelity and global harmony.
citing papers explorer
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AIM-Bench: Benchmarking and Improving Affective Image Manipulation via Fine-Grained Hierarchical Control
AIM-Bench is the first dedicated benchmark for editing images to evoke specific emotions with fine-grained control, paired with AIM-40k dataset that delivers a 9.15% performance gain by correcting training data imbalances.
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Delta Rectified Flow Sampling for Text-to-Image Editing
DRFS is a new inversion-free editing technique for rectified flow models that models source-target velocity discrepancies and applies a time-dependent shift to improve fidelity and unify prior methods like DDS and FlowEdit.
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NeoMap: Training-free Novel-View Synthesis from Single Images and Videos
NeoMap introduces a training-free framework using convergent manifold alternating projection iterations to extract high-fidelity novel views from pre-trained video models, outperforming prior methods on standard benchmarks.
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Zero-Shot Generative De-identification: Inversion-Free Flow for Privacy-Preserving Skin Image Analysis
Zero-shot inversion-free flow method de-identifies skin images in under 20 seconds while preserving pathological features with IoU stability exceeding 0.67 using segment-by-synthesis and CIELAB decoupling.
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SketchDeco: Training-Free Latent Composition for Precise Sketch Colourisation
SketchDeco performs training-free sketch colourisation via diffusion inversion to insert user colors followed by custom self-attention blending for local fidelity and global harmony.
- Functionalization via Structure Completion and Motion Rectification