MakeupMirror reports 60% better facial similarity and 50% less skin tone change than Stable-Makeup using geometry, region, and tone controls in diffusion models.
FLUX-Makeup: High-fidelity, identity-consistent, and robust makeup transfer via diffusion transformer
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
Aligning the DDIM forward diffusion process with flow-matching manifold evolution enables high-quality generation without time conditioning, and class-conditional synthesis is possible with an unconditional denoiser by using separate time spaces per class.
citing papers explorer
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MakeupMirror: Improving Facial Attribute Preservation in Diffusion Models for Makeup Transfer
MakeupMirror reports 60% better facial similarity and 50% less skin tone change than Stable-Makeup using geometry, region, and tone controls in diffusion models.
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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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Exploring Time Conditioning in Diffusion Generative Models from Disjoint Noisy Data Manifolds
Aligning the DDIM forward diffusion process with flow-matching manifold evolution enables high-quality generation without time conditioning, and class-conditional synthesis is possible with an unconditional denoiser by using separate time spaces per class.