A 3D-warped synthetic triplet dataset plus GRPO post-training on real portraits yields state-of-the-art identity-preserving makeup transfer, evaluated on a new diverse BeautyBench benchmark.
Consistent image layout editing with diffusion models
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DDA-Thinker decouples planning from generation and applies dual-atomic RL with checklist-based rewards to boost reasoning in image editing, yielding competitive results on RISE-Bench and KRIS-Bench.
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From Synthetic to Real: Toward Identity-Consistent Makeup Transfer with Synthetic and Real Data
A 3D-warped synthetic triplet dataset plus GRPO post-training on real portraits yields state-of-the-art identity-preserving makeup transfer, evaluated on a new diverse BeautyBench benchmark.
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DDA-Thinker: Decoupled Dual-Atomic Reinforcement Learning for Reasoning-Driven Image Editing
DDA-Thinker decouples planning from generation and applies dual-atomic RL with checklist-based rewards to boost reasoning in image editing, yielding competitive results on RISE-Bench and KRIS-Bench.