Eduarda Caldeira — accepted AI-conference papers

2026

AdaptDiff: Adaptive Guidance in Diffusion Models for Diverse and Identity-Consistent Face Synthesis (Student Abstract)

Eduarda Caldeira, Tahar Chettaoui, Naser Damer, Fadi Boutros

AAAI 2026technical

Diffusion models conditioned on identity embeddings enable the generation of synthetic face images that consistently preserve identity across multiple samples. Recent work has shown that introducing an additional negative condition through classifier-free guidance during sampling provides a mechanis

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2026

IDperturb: Enhancing Variation in Synthetic Face Generation via Angular Perturbations

Fadi Boutros, Eduarda Caldeira, Tahar Chettaoui, Naser Damer

CVPR 2026

Synthetic data has emerged as a practical alternative to authentic face datasets for training face recognition (FR) systems, especially as privacy and legal concerns increasingly restrict the use of real biometric data. Recent advances in identity-conditional diffusion models have enabled the genera

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