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arXiv Computer Vision

Adaptive Adversarial Augmentation for Controllable Face Synthesis

The paper introduces Ensemble Feedback Controllable Synthesis (EFCS), a framework that generates diverse, challenging synthetic face images while preserving visual realism. EFCS improves distributional variability, achieving higher FID and KID scores than single‑feedback or random synthesis, yet maintains high precision. Models trained on EFCS data outperform baselines on multiple benchmarks, and the authors provide an analytical formulation linking perturbation difficulty, sample utility, and performance degradation to guide synthetic data complexity.

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arXiv Machine Learning
Sep 4

A Real-Calibrated Synthetic-First Data Engine

The paper introduces the Real‑Calibrated Synthetic‑First Data Engine, a modular pipeline that integrates controllable diffusion‑based synthetic image generation with multi‑stage curation, filtering, and optional uncertainty‑driven selection and human verification. Designed as a CLI‑based framework, it allows independent configuration of generation, filtering, selection, and validation modules to enhance reproducibility and flexibility in real‑world data workflows. Empirical tests on human pose estimation demonstrate that synthetic data can boost a real‑data baseline when used as low‑cost augmentation, though synthetic‑only training still lags behind real‑only performance, underscoring the importance of data‑centric orchestration in low‑data regimes.

By Yukang Shen, Zhiguo Liu, Yingshu Li, Yan Huang
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arXiv Computer Vision
Sep 25

Long-Tail Adaptive Flow Matching with Explicit Conditional Consistency Guidance for Precise Multimodal Face Synthesis

The paper introduces EC²Face, a multimodal face synthesis framework that enhances semantic alignment by combining Explicit Conditional Consistency Guidance (ECCG) and Long‑Tail Adaptive Flow Matching (LAFM). ECCG enforces pixel‑level consistency between generated faces, textual descriptions, and semantic masks, while a temporal dynamic modulation adjusts supervision strength over diffusion timesteps. LAFM reweights spatial optimization signals according to attribute frequency, improving rare attribute synthesis without adding inference overhead. Experiments demonstrate that EC²Face outperforms baselines, achieving a 29.38% improvement in mask accuracy for rare attributes.

By Yushe Cao, Xuechao Zou, Xing Xi, Dianxi Shi, Chun Yu, Junliang Xing
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arXiv AI
Sep 4

ScoreMix: Synthetic Data Generation by Score Composition in Diffusion Models Improves Recognition

ScoreMix is a self‑contained synthetic data generation method that improves recognition tasks by mixing class‑conditioned scores along reverse diffusion trajectories, thereby creating hard synthetic samples without external resources. The approach shows that selecting classes far apart in the discriminator’s embedding space yields larger performance gains, up to 3% more improvement than proximity‑based selection. Across eight public face recognition benchmarks, ScoreMix boosts accuracy by up to 7 percentage points, demonstrating robustness and practicality without hyperparameter tuning.

By Parsa Rahimi, Sebastien Marcel
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