no code implementations • 28 Nov 2023 • Yufeng Zheng, Xueting Li, Koki Nagano, Sifei Liu, Karsten Kreis, Otmar Hilliges, Shalini De Mello
Large-scale diffusion generative models are greatly simplifying image, video and 3D asset creation from user-provided text prompts and images.
1 code implementation • 26 Oct 2023 • Shrisha Bharadwaj, Yufeng Zheng, Otmar Hilliges, Michael J. Black, Victoria Fernandez-Abrevaya
Our goal is to efficiently learn personalized animatable 3D head avatars from videos that are geometrically accurate, realistic, relightable, and compatible with current rendering systems.
no code implementations • 24 Dec 2022 • Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng, Pushi Zhang, Li Zhao, Wenxue Cheng, Peng Cheng, Yongqiang Xiong, Tao Qin, Jianyu Chen, Tie-Yan Liu
One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated bandwidth in congestion control.
1 code implementation • CVPR 2023 • Yufeng Zheng, Wang Yifan, Gordon Wetzstein, Michael J. Black, Otmar Hilliges
The ability to create realistic, animatable and relightable head avatars from casual video sequences would open up wide ranging applications in communication and entertainment.
1 code implementation • CVPR 2022 • Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C. Bühler, Xu Chen, Michael J. Black, Otmar Hilliges
Traditional 3D morphable face models (3DMMs) provide fine-grained control over expression but cannot easily capture geometric and appearance details.
1 code implementation • ICCV 2021 • Xu Chen, Yufeng Zheng, Michael J. Black, Otmar Hilliges, Andreas Geiger
However, this is problematic since the backward warp field is pose dependent and thus requires large amounts of data to learn.
no code implementations • 27 Mar 2021 • Yufeng Zheng, Yunkai Zhang, Zeyu Zheng
To partially alleviate this difficulty, we propose a simple generator regularization term on the GAN generator loss in the form of Lipschitz penalty.
1 code implementation • 27 Dec 2020 • Yufeng Zheng, Zeyu Zheng, Tingyu Zhu
We propose a doubly stochastic simulator that integrates a stochastic generative neural network and a classical Monte Carlo Poisson simulator, to utilize both advantages.
2 code implementations • NeurIPS 2020 • Yufeng Zheng, Seonwook Park, Xucong Zhang, Shalini De Mello, Otmar Hilliges
Furthermore, we show that in the presence of limited amounts of real-world training data, our method allows for improvements in the downstream task of semi-supervised cross-dataset gaze estimation.