Yung-Yu Chuang

By intelligently guiding the diffusion process with uncertainty estimates, UGDiff achieves a groundbreaking balance between perceptual realism and fidelity in super-resolution tasks.

Training generative inverse renderers on AAA game footage closes the realism gap, enabling superior generalization and controllable generation compared to models trained on existing synthetic datasets.