In this work, we propose an ID-preserving talking head generation framework, which advances previous methods in two aspects.
Recent multi-output inference works propagate the bidirectional temporal feature with a parallel or recurrent framework, which either suffers from performance drops on the temporal edges of clips or can not achieve online inference.
Ranked #1 on Video Denoising on CRVD
We present a new data-driven approach with physics-based priors to scene-level normal estimation from a single polarization image.
Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amount of real-world data, which makes it difficult to evaluate the strengths or weaknesses of different reflection removal methods thoroughly.