no code implementations • 9 Aug 2023 • Liyang Chen, Zhiyong Wu, Runnan Li, Weihong Bao, Jun Ling, Xu Tan, Sheng Zhao
With our essential designs on facial style learning, our model is able to flexibly capture the expressive facial style from arbitrary video prompts and transfer it onto a personalized image renderer in a zero-shot manner.
no code implementations • ICCV 2023 • Zenghao Chai, Tianke Zhang, Tianyu He, Xu Tan, Tadas Baltrušaitis, HsiangTao Wu, Runnan Li, Sheng Zhao, Chun Yuan, Jiang Bian
3D Morphable Models (3DMMs) demonstrate great potential for reconstructing faithful and animatable 3D facial surfaces from a single image.
Ranked #1 on 3D Face Reconstruction on REALY (side-view)
no code implementations • 30 Jan 2023 • Shengmeng Li, Luping Liu, Zenghao Chai, Runnan Li, Xu Tan
Different from the traditional predictor based on explicit Adams methods, we leverage a Lagrange interpolation function as the predictor, which is further enhanced with an error-robust strategy to adaptively select the Lagrange bases with lower error in the estimated noise.
no code implementations • 29 Aug 2022 • Jun Ling, Xu Tan, Liyang Chen, Runnan Li, Yuchao Zhang, Sheng Zhao, Li Song
In this paper, we conduct systematic analyses on the motion jittering problem based on a state-of-the-art pipeline that uses 3D face representations to bridge the input audio and output video, and improve the motion stability with a series of effective designs.