219 citations · 1.2k across the 35 of their papers we have counts for
35 papers
Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On
Daiheng Gao, Xu Chen, Xindi Zhang +5
Fabricating and designing 3D garments has become extremely demanding with the increasing need for synthesizing realistic dressed persons for a variety of applications, e.g. 3D virt…
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer
Yuan Dong, Chuan Fang, Liefeng Bo +2
Panoramic image enables deeper understanding and more holistic perception of surrounding environment, which can naturally encode enriched scene context information comp…
DiffHand: End-to-End Hand Mesh Reconstruction via Diffusion Models
Lijun Li, Li'an Zhuo, Bang Zhang +2
Hand mesh reconstruction from the monocular image is a challenging task due to its depth ambiguity and severe occlusion, there remains a non-unique mapping between the monocular im…
One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field
Weichuang Li, Longhao Zhang, Dong Wang +7
Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image. Most pioneering methods rely…
Evaluate Geometry of Radiance Fields with Low-frequency Color Prior
Qihang Fang, Yafei Song, Keqiang Li +4
A radiance field is an effective representation of 3D scenes, which has been widely adopted in novel-view synthesis and 3D reconstruction. It is still an open and challenging probl…
Multi-Behavior Graph Neural Networks for Recommender System
Lianghao Xia, Chao Huang, Yong Xu +2
Recommender systems have been demonstrated to be effective to meet user's personalized interests for many online services (e.g., E-commerce and online advertising platforms). Recen…
4K-NeRF: High Fidelity Neural Radiance Fields at Ultra High Resolutions
Zhongshu Wang, Lingzhi Li, Zhen Shen +2
In this paper, we present a novel and effective framework, named 4K-NeRF, to pursue high fidelity view synthesis on the challenging scenarios of ultra high resolutions, building on…
Compressing Volumetric Radiance Fields to 1 MB
Lingzhi Li, Zhen Shen, Zhongshu Wang +2
Approximating radiance fields with volumetric grids is one of promising directions for improving NeRF, represented by methods like Plenoxels and DVGO, which achieve super-fast trai…
A-ACT: Action Anticipation through Cycle Transformations
Akash Gupta, Jingen Liu, Liefeng Bo +2
While action anticipation has garnered a lot of research interest recently, most of the works focus on anticipating future action directly through observed visual cues only. In thi…
An Efficient and Robust System for Vertically Federated Random Forest
Houpu Yao, Jiazhou Wang, Peng Dai +2
As there is a growing interest in utilizing data across multiple resources to build better machine learning models, many vertically federated learning algorithms have been proposed…
Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling
Lianghao Xia, Chao Huang, Yong Xu +3
Many previous studies aim to augment collaborative filtering with deep neural network techniques, so as to achieve better recommendation performance. However, most existing deep le…
Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning
Lianghao Xia, Chao Huang, Yong Xu +4
Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, cri…
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Xiyue Zhang, Chao Huang, Yong Xu +5
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public ri…
Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network
Lianghao Xia, Chao Huang, Yong Xu +3
Capturing users' precise preferences is of great importance in various recommender systems (eg., e-commerce platforms), which is the basis of how to present personalized interestin…
Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation
Lianghao Xia, Chao Huang, Yong Xu +5
Accurate user and item embedding learning is crucial for modern recommender systems. However, most existing recommendation techniques have thus far focused on modeling users' prefe…
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
Chao Huang, Jiahui Chen, Lianghao Xia +6
Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services. However, the majority of existi…
Knowledge-aware Coupled Graph Neural Network for Social Recommendation
Chao Huang, Huance Xu, Yong Xu +7
Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborat…
Graph Meta Network for Multi-Behavior Recommendation
Lianghao Xia, Yong Xu, Chao Huang +2
Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions. In practical recommendation scenarios, use…
Social Recommendation with Self-Supervised Metagraph Informax Network
Xiaoling Long, Chao Huang, Yong Xu +4
In recent years, researchers attempt to utilize online social information to alleviate data sparsity for collaborative filtering, based on the rationale that social networks offers…
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization
Qingsong Zhang, Bin Gu, Cheng Deng +4
Vertical federated learning (VFL) is an effective paradigm of training the emerging cross-organizational (e.g., different corporations, companies and organizations) collaborative l…
Memory-Augmented Non-Local Attention for Video Super-Resolution
Jiyang Yu, Jingen Liu, Liefeng Bo +1
In this paper, we propose a novel video super-resolution method that aims at generating high-fidelity high-resolution (HR) videos from low-resolution (LR) ones. Previous methods pr…
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
Bo Liu, Chaowei Tan, Jiazhou Wang +7
In this paper, we present Fedlearn-Algo, an open-source privacy preserving machine learning platform. We use this platform to demonstrate our research and development results on pr…
Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark
Longyin Wen, Dawei Du, Pengfei Zhu +4
To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured largescale dataset, n…
Data Augmentation for Object Detection via Differentiable Neural Rendering
Guanghan Ning, Guang Chen, Chaowei Tan +3
It is challenging to train a robust object detector under the supervised learning setting when the annotated data are scarce. Thus, previous approaches tackling this problem are in…