61 citations · 576 across the 47 of their papers we have counts for
7 papers · 1 filter
FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction
Qiao Feng, Yebin Liu, Yu-Kun Lai +2
The advent of deep learning has led to significant progress in monocular human reconstruction. However, existing representations, such as parametric models, voxel grids, meshes and…
StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning
Yi-Hua Huang, Yue He, Yu-Jie Yuan +2
3D scene stylization aims at generating stylized images of the scene from arbitrary novel views following a given set of style examples, while ensuring consistency when rendered fr…
NeRF-Editing: Geometry Editing of Neural Radiance Fields
Yu-Jie Yuan, Yang-Tian Sun, Yu-Kun Lai +3
Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great potential in novel view synthesis of a scene. However, current NeRF-based methods cannot enable…
Playing Lottery Tickets in Style Transfer Models
Meihao Kong, Jing Huo, Wenbin Li +3
Style transfer has achieved great success and attracted a wide range of attention from both academic and industrial communities due to its flexible application scenarios. However,…
Quality Metric Guided Portrait Line Drawing Generation from Unpaired Training Data
Ran Yi, Yong-Jin Liu, Yu-Kun Lai +1
Face portrait line drawing is a unique style of art which is highly abstract and expressive. However, due to its high semantic constraints, many existing methods learn to generate…
MuSCLe: A Multi-Strategy Contrastive Learning Framework for Weakly Supervised Semantic Segmentation
Kunhao Yuan, Gerald Schaefer, Yu-Kun Lai +4
Weakly supervised semantic segmentation (WSSS) has gained significant popularity since it relies only on weak labels such as image level annotations rather than pixel level annotat…
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…