EDGAR: Efficient Diffusion-Driven Gaussian Avatar Refinement via Synthetic Failure

EDGAR is a conditional diffusion framework for refining Gaussian avatars by correcting artifacts while preserving identity fidelity, trained on EDGAR-SET, a hybrid dataset of real and synthetic samples simulating avatar failures.