34 citations · 65 across the 17 of their papers we have counts for
7 papers · 1 filter
Pose-Free Neural Radiance Fields via Implicit Pose Regularization
Jiahui Zhang, Fangneng Zhan, Yingchen Yu +5
Pose-free neural radiance fields (NeRF) aim to train NeRF with unposed multi-view images and it has achieved very impressive success in recent years. Most existing works share the…
WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields
Muyu Xu, Fangneng Zhan, Jiahui Zhang +5
Neural Radiance Field (NeRF) has shown impressive performance in novel view synthesis via implicit scene representation. However, it usually suffers from poor scalability as requir…
Weakly Supervised 3D Open-vocabulary Segmentation
Kunhao Liu, Fangneng Zhan, Jiahui Zhang +6
Open-vocabulary segmentation of 3D scenes is a fundamental function of human perception and thus a crucial objective in computer vision research. However, this task is heavily impe…
Audio-Driven Talking Face Generation with Diverse yet Realistic Facial Animations
Rongliang Wu, Yingchen Yu, Fangneng Zhan +3
Audio-driven talking face generation, which aims to synthesize talking faces with realistic facial animations (including accurate lip movements, vivid facial expression details and…
POCE: Pose-Controllable Expression Editing
Rongliang Wu, Yingchen Yu, Fangneng Zhan +3
Facial expression editing has attracted increasing attention with the advance of deep neural networks in recent years. However, most existing methods suffer from compromised editin…
StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields
Kunhao Liu, Fangneng Zhan, Yiwen Chen +5
3D style transfer aims to render stylized novel views of a 3D scene with multi-view consistency. However, most existing work suffers from a three-way dilemma over accurate geometry…
Regularized Vector Quantization for Tokenized Image Synthesis
Jiahui Zhang, Fangneng Zhan, Christian Theobalt +1
Quantizing images into discrete representations has been a fundamental problem in unified generative modeling. Predominant approaches learn the discrete representation either in a…