CA-IDD is the first diffusion model for face swapping that integrates multi-modal cross-attention guidance from identity embeddings, gaze, and facial parsing to achieve better identity consistency and an FID of 11.73 over GAN baselines.
A style-based generator architecture for generative adversarial networks,
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Implicit generative choices in diffusion models for ambiguous prompts are localized principally in self-attention layers, enabling a targeted ICM steering method that outperforms prior debiasing approaches.
PaCo-FR introduces a structured-masking and patch-codebook framework for unsupervised facial representation pre-training that claims state-of-the-art results on multiple facial tasks after training on only 2 million unlabeled images.
citing papers explorer
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CA-IDD: Cross-Attention Guided Identity-Conditional Diffusion for Identity-Consistent Face Swapping
CA-IDD is the first diffusion model for face swapping that integrates multi-modal cross-attention guidance from identity embeddings, gaze, and facial parsing to achieve better identity consistency and an FID of 11.73 over GAN baselines.
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Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models
Implicit generative choices in diffusion models for ambiguous prompts are localized principally in self-attention layers, enabling a targeted ICM steering method that outperforms prior debiasing approaches.
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PaCo-FR: Patch-Pixel Aligned End-to-End Codebook Learning for Facial Representation Pre-training
PaCo-FR introduces a structured-masking and patch-codebook framework for unsupervised facial representation pre-training that claims state-of-the-art results on multiple facial tasks after training on only 2 million unlabeled images.