261 citations · 377 across the 13 of their papers we have counts for
15 papers
Neural Gradient Regularizer
Shuang Xu, Yifan Wang, Zixiang Zhao +6
Owing to its significant success, the prior imposed on gradient maps has consistently been a subject of great interest in the field of image processing. Total variation (TV), one o…
Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning
Junwen He, Yifan Wang, Lijun Wang +6
Depth-aware panoptic segmentation is an emerging topic in computer vision which combines semantic and geometric understanding for more robust scene interpretation. Recent works pur…
Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation
Yichen Yuan, Yifan Wang, Lijun Wang +5
Recent leading zero-shot video object segmentation (ZVOS) works devote to integrating appearance and motion information by elaborately designing feature fusion modules and identica…
Detector-Free Structure from Motion
Xingyi He, Jiaming Sun, Yifan Wang +4
We propose a new structure-from-motion framework to recover accurate camera poses and point clouds from unordered images. Traditional SfM systems typically rely on the successful d…
BEV-IO: Enhancing Bird's-Eye-View 3D Detection with Instance Occupancy
Zaibin Zhang, Yuanhang Zhang, Lijun Wang +2
A popular approach for constructing bird's-eye-view (BEV) representation in 3D detection is to lift 2D image features onto the viewing frustum space based on explicitly predicted d…
LumiGAN: Unconditional Generation of Relightable 3D Human Faces
Boyang Deng, Yifan Wang, Gordon Wetzstein
Unsupervised learning of 3D human faces from unstructured 2D image data is an active research area. While recent works have achieved an impressive level of photorealism, they commo…
PointAvatar: Deformable Point-based Head Avatars from Videos
Yufeng Zheng, Wang Yifan, Gordon Wetzstein +2
The ability to create realistic, animatable and relightable head avatars from casual video sequences would open up wide ranging applications in communication and entertainment. Cur…
Generative Neural Articulated Radiance Fields
Alexander W. Bergman, Petr Kellnhofer, Wang Yifan +3
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress. These 3D GANs, h…
Input-level Inductive Biases for 3D Reconstruction
Wang Yifan, Carl Doersch, Relja Arandjelović +2
Much of the recent progress in 3D vision has been driven by the development of specialized architectures that incorporate geometrical inductive biases. In this paper we tackle 3D r…
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields
Wang Yifan, Lukas Rahmann, Olga Sorkine-Hornung
We present implicit displacement fields, a novel representation for detailed 3D geometry. Inspired by a classic surface deformation technique, displacement mapping, our method repr…
Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations
Wang Yifan, Shihao Wu, Cengiz Oztireli +1
Neural implicit functions have emerged as a powerful representation for surfaces in 3D. Such a function can encode a high quality surface with intricate details into the parameters…
Neural Cages for Detail-Preserving 3D Deformations
Wang Yifan, Noam Aigerman, Vladimir G. Kim +2
We propose a novel learnable representation for detail-preserving shape deformation. The goal of our method is to warp a source shape to match the general structure of a target sha…
Differentiable Surface Splatting for Point-based Geometry Processing
Wang Yifan, Felice Serena, Shihao Wu +2
We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for point clouds. Gradients for point locations and normals are carefully designed to han…
Patch-based Progressive 3D Point Set Upsampling
Wang Yifan, Shihao Wu, Hui Huang +2
We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by…
A Fully Progressive Approach to Single-Image Super-Resolution
Yifan Wang, Federico Perazzi, Brian McWilliams +3
Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each ca…