Jun 22, 2022 · We propose a Transformer-based prediction network, named CodeFormer, to model the global composition and context of the low-quality faces for code prediction.
In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping ...
This project is based on BasicSR. Some codes are brought from Unleashing Transformers, YOLOv5-face, and FaceXLib. We also adopt Real-ESRGAN to support ...
Oct 31, 2022 · In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration ...
Apr 3, 2024 · We propose a Transformer-based prediction network, named CodeFormer, to model the global composition and context of the low-quality faces for code prediction.
Mar 10, 2023 · In this paper, we demonstrate that the learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of ...
Blind face restoration is a highly ill-posed problem that often requires auxiliary guidance to 1) improve the mapping from degraded inputs to desired ...
We propose a Transformer-based prediction network, named CodeFormer, to model global composition and context of the low-quality faces for code prediction.
We propose a Transformer-based prediction network, named CodeFormer, to model the global composition and context of the low-quality faces for code prediction.
Duration: 3:28
Posted: Sep 17, 2022
Posted: Sep 17, 2022