Towards Practical Capture of High-Fidelity Relightable Avatars

no code implementations • 8 Sep 2023 • Haotian Yang, Mingwu Zheng, Wanquan Feng, Haibin Huang, Yu-Kun Lai, Pengfei Wan, Zhongyuan Wang, Chongyang Ma

Specifically, TRAvatar is trained with dynamic image sequences captured in a Light Stage under varying lighting conditions, enabling realistic relighting and real-time animation for avatars in diverse scenes.

Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera

1 code implementation • 30 Jun 2022 • Hongrui Cai, Wanquan Feng, Xuetao Feng, Yan Wang, Juyong Zhang

We propose Neural-DynamicReconstruction (NDR), a template-free method to recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D camera.

Dynamic Reconstruction Monocular Reconstruction +2

Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling

1 code implementation • CVPR 2022 • Wanquan Feng, Jin Li, Hongrui Cai, Xiaonan Luo, Juyong Zhang

Different from traditional point cloud representation where each point only represents a position or a local plane in the 3D space, each point in Neural Points represents a local continuous geometric shape via neural fields.

point cloud upsampling

Recurrent Multi-view Alignment Network for Unsupervised Surface Registration

1 code implementation • CVPR 2021 • Wanquan Feng, Juyong Zhang, Hongrui Cai, Haofei Xu, Junhui Hou, Hujun Bao

Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data.

Deformable Object Manipulation Neural Rendering +1