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.
Gans trained by a two time-scale update rule converge to a local nash equilib- rium
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Self-Swap Guidance steers diffusion sampling by swapping dissimilar token latents to enable CFG-like improvements for both conditional and unconditional generation.
citing papers explorer
-
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.
-
Guiding a Diffusion Model by Swapping Its Tokens
Self-Swap Guidance steers diffusion sampling by swapping dissimilar token latents to enable CFG-like improvements for both conditional and unconditional generation.