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.
High-resolution image syn- thesis with latent diffusion models
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A coarse-to-fine pipeline deforms 3D meshes to reflect geometric features from an image using diffusion model representations while preserving topology and part-level semantics.
A framework that applies provenance-based guidance to input gradients during synthetic data training to promote learning from target regions only.
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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Image-Guided Geometric Stylization of 3D Meshes
A coarse-to-fine pipeline deforms 3D meshes to reflect geometric features from an image using diffusion model representations while preserving topology and part-level semantics.
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Learning from Synthetic Data via Provenance-Based Input Gradient Guidance
A framework that applies provenance-based guidance to input gradients during synthetic data training to promote learning from target regions only.