activity
20182022
most citedReconstructing Personalized Semantic Facial NeRF Models From Monocular Video

130 citations · 374 across the 22 of their papers we have counts for

collaborators

37 papers

cs.GR2022130 cited

Reconstructing Personalized Semantic Facial NeRF Models From Monocular Video

Xuan Gao, Chenglai Zhong, Jun Xiang +3

We present a novel semantic model for human head defined with neural radiance field. The 3D-consistent head model consist of a set of disentangled and interpretable bases, and can…

cs.CV20224 cited

SelfNeRF: Fast Training NeRF for Human from Monocular Self-rotating Video

Bo Peng, Jun Hu, Jingtao Zhou +1

In this paper, we propose SelfNeRF, an efficient neural radiance field based novel view synthesis method for human performance. Given monocular self-rotating videos of human perfor…

cs.CV2022

A Survey of Non-Rigid 3D Registration

Bailin Deng, Yuxin Yao, Roberto M. Dyke +1

Non-rigid registration computes an alignment between a source surface with a target surface in a non-rigid manner. In the past decade, with the advances in 3D sensing technologies…

cs.CV202255 cited

Audio-Driven Talking Face Video Generation with Dynamic Convolution Kernels

Zipeng Ye, Mengfei Xia, Ran Yi +5

In this paper, we present a dynamic convolution kernel (DCK) strategy for convolutional neural networks. Using a fully convolutional network with the proposed DCKs, high-quality ta…

cs.GR2021

GeodesicEmbedding (GE): A High-Dimensional Embedding Approach for Fast Geodesic Distance Queries

Qianwei Xia, Juyong Zhang, Zheng Fang +4

In this paper, we develop a novel method for fast geodesic distance queries. The key idea is to embed the mesh into a high-dimensional space, such that the Euclidean distance in th…

cs.CV2021

A Robust Loss for Point Cloud Registration

Zhi Deng, Yuxin Yao, Bailin Deng +1

The performance of surface registration relies heavily on the metric used for the alignment error between the source and target shapes. Traditionally, such a metric is based on the…