318 citations · 839 across the 46 of their papers we have counts for
47 papers
NeMF: Inverse Volume Rendering with Neural Microflake Field
Youjia Zhang, Teng Xu, Junqing Yu +5
Recovering the physical attributes of an object's appearance from its images captured under an unknown illumination is challenging yet essential for photo-realistic rendering. Rece…
Generative Deformable Radiance Fields for Disentangled Image Synthesis of Topology-Varying Objects
Ziyu Wang, Yu Deng, Jiaolong Yang +2
3D-aware generative models have demonstrated their superb performance to generate 3D neural radiance fields (NeRF) from a collection of monocular 2D images even for topology-varyin…
Disentangling Light Fields for Super-Resolution and Disparity Estimation
Yingqian Wang, Longguang Wang, Gaochang Wu +4
Light field (LF) cameras record both intensity and directions of light rays, and encode 3D scenes into 4D LF images. Recently, many convolutional neural networks (CNNs) have been p…
Light Field-Based Underwater 3D Reconstruction Via Angular Resampling
Yuqi Ding, Zhang Chen, Yu Ji +2
Recovering 3D geometry of underwater scenes is challenging because of non-linear refraction of light at the water-air interface caused by the camera housing. We present a light fie…
iButter: Neural Interactive Bullet Time Generator for Human Free-viewpoint Rendering
Liao Wang, Ziyu Wang, Pei Lin +5
Generating ``bullet-time'' effects of human free-viewpoint videos is critical for immersive visual effects and VR/AR experience. Recent neural advances still lack the controllable…
Neural Free-Viewpoint Performance Rendering under Complex Human-object Interactions
Guoxing Sun, Xin Chen, Yizhang Chen +6
4D reconstruction of human-object interaction is critical for immersive VR/AR experience and human activity understanding. Recent advances still fail to recover fine geometry and t…
Neural Relighting and Expression Transfer On Video Portraits
Youjia Wang, Taotao Zhou, Minzhang Li +4
Photo-realistic video portrait reenactment benefits virtual production and numerous VR/AR experiences. The task remains challenging as the reenacted expression should match the sou…
Few-shot Neural Human Performance Rendering from Sparse RGBD Videos
Anqi Pang, Xin Chen, Haimin Luo +3
Recent neural rendering approaches for human activities achieve remarkable view synthesis results, but still rely on dense input views or dense training with all the capture frames…
IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation
Qing Wu, Yuwei Li, Lan Xu +7
For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images an…
PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging
Yuwei Li, Minye Wu, Yuyao Zhang +2
Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare. Existing parametric models account only for hand shape, pose, or texture, without modeling…
MirrorNeRF: One-shot Neural Portrait Radiance Field from Multi-mirror Catadioptric Imaging
Ziyu Wang, Liao Wang, Fuqiang Zhao +3
Photo-realistic neural reconstruction and rendering of the human portrait are critical for numerous VR/AR applications. Still, existing solutions inherently rely on multi-view capt…
Editable Free-viewpoint Video Using a Layered Neural Representation
Jiakai Zhang, Xinhang Liu, Xinyi Ye +6
Generating free-viewpoint videos is critical for immersive VR/AR experience but recent neural advances still lack the editing ability to manipulate the visual perception for large…
Convolutional Neural Opacity Radiance Fields
Haimin Luo, Anpei Chen, Qixuan Zhang +4
Photo-realistic modeling and rendering of fuzzy objects with complex opacity are critical for numerous immersive VR/AR applications, but it suffers from strong view-dependent brigh…
SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos
Xin Chen, Anqi Pang, Wei Yang +3
Markerless motion capture and understanding of professional non-daily human movements is an important yet unsolved task, which suffers from complex motion patterns and severe self-…
Neural Video Portrait Relighting in Real-time via Consistency Modeling
Longwen Zhang, Qixuan Zhang, Minye Wu +2
Video portraits relighting is critical in user-facing human photography, especially for immersive VR/AR experience. Recent advances still fail to recover consistent relit result un…
GNeRF: GAN-based Neural Radiance Field without Posed Camera
Quan Meng, Anpei Chen, Haimin Luo +5
We introduce GNeRF, a framework to marry Generative Adversarial Networks (GAN) with Neural Radiance Field (NeRF) reconstruction for the complex scenarios with unknown and even rand…
MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo
Anpei Chen, Zexiang Xu, Fuqiang Zhao +4
We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct neural radiance fields for view synthesis. Unlike prior works on neural radiance fields that…
SofGAN: A Portrait Image Generator with Dynamic Styling
Anpei Chen, Ruiyang Liu, Ling Xie +3
Recently, Generative Adversarial Networks (GANs)} have been widely used for portrait image generation. However, in the latent space learned by GANs, different attributes, such as p…
AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
Xin Chen, Yuwei Li, Xi Luo +4
This paper presents a fully automatic framework for extracting editable 3D objects directly from a single photograph. Unlike previous methods which recover either depth maps, point…
Spatial-Angular Interaction for Light Field Image Super-Resolution
Yingqian Wang, Longguang Wang, Jungang Yang +3
Light field (LF) cameras record both intensity and directions of light rays, and capture scenes from a number of viewpoints. Both information within each perspective (i.e., spatial…
A Neural Rendering Framework for Free-Viewpoint Relighting
Zhang Chen, Anpei Chen, Guli Zhang +4
We present a novel Relightable Neural Renderer (RNR) for simultaneous view synthesis and relighting using multi-view image inputs. Existing neural rendering (NR) does not explicitl…
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware Fusion
Jing Jin, Junhui Hou, Jie Chen +3
A densely-sampled light field (LF) is highly desirable in various applications, such as 3-D reconstruction, post-capture refocusing and virtual reality. However, it is costly to ac…
Light Field Super-resolution via Attention-Guided Fusion of Hybrid Lenses
Jing Jin, Junhui Hou, Jie Chen +2
This paper explores the problem of reconstructing high-resolution light field (LF) images from hybrid lenses, including a high-resolution camera surrounded by multiple low-resoluti…
Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation
Ziheng Zhang, Anpei Chen, Ling Xie +2
In this work, we demonstrate yet another approach to tackle the amodal segmentation problem. Specifically, we first introduce a new representation, namely a semantics-aware distanc…