Proposes IaD framework with Identity Decoupling Loss and Text Alignment Loss for richer, identity-consistent IPT2V without subject-specific fine-tuning.
Real-time identity defenses against malicious personalization of diffusion models
4 Pith papers cite this work. Polarity classification is still indexing.
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A two-stage framework that predicts a Canny edge map before rendering improves preservation of high-frequency identity details (logos, patterns, text) in subject-driven image generation.
Edit-R1 builds a CoT-based reasoning reward model (RRM) via SFT and GCPO, then applies it with GRPO to improve image editing models such as FLUX.1-kontext.
Proposes the SFR framework and InfoSqueeze module to resolve Interest Entanglement by decoupling regression and perceptual objectives in image super-resolution through shared feature representations.
citing papers explorer
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Customizing Video Portraits via Identity-ActionDecoupling
Proposes IaD framework with Identity Decoupling Loss and Text Alignment Loss for richer, identity-consistent IPT2V without subject-specific fine-tuning.
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Decomposing Subject-Driven Image Generation via Intermediate Structural Prediction
A two-stage framework that predicts a Canny edge map before rendering improves preservation of high-frequency identity details (logos, patterns, text) in subject-driven image generation.
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Leveraging Verifier-Based Reinforcement Learning in Image Editing
Edit-R1 builds a CoT-based reasoning reward model (RRM) via SFT and GCPO, then applies it with GRPO to improve image editing models such as FLUX.1-kontext.
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Interest Entanglement: The Hidden Barrier to Blind Super-Resolution Optimization
Proposes the SFR framework and InfoSqueeze module to resolve Interest Entanglement by decoupling regression and perceptual objectives in image super-resolution through shared feature representations.