10 citations · 37 across the 17 of their papers we have counts for
22 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…
TO-Rawnet: Improving RawNet with TCN and Orthogonal Regularization for Fake Audio Detection
Chenglong Wang, Jiangyan Yi, Jianhua Tao +4
Current fake audio detection relies on hand-crafted features, which lose information during extraction. To overcome this, recent studies use direct feature extraction from raw audi…
Detection of Cross-Dataset Fake Audio Based on Prosodic and Pronunciation Features
Chenglong Wang, Jiangyan Yi, Jianhua Tao +3
Existing fake audio detection systems perform well in in-domain testing, but still face many challenges in out-of-domain testing. This is due to the mismatch between the training a…
Cross-domain Augmentation Networks for Click-Through Rate Prediction
Xu Chen, Zida Cheng, Shuai Xiao +2
Data sparsity is an important issue for click-through rate (CTR) prediction, particularly when user-item interactions is too sparse to learn a reliable model. Recently, many works…
AG3D: Learning to Generate 3D Avatars from 2D Image Collections
Zijian Dong, Xu Chen, Jinlong Yang +3
While progress in 2D generative models of human appearance has been rapid, many applications require 3D avatars that can be animated and rendered. Unfortunately, most existing meth…
Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy
Xiaohai Hu, Aparajit Venkatesh, Yusen Wan +5
Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for gr…
Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition
Chen Guo, Tianjian Jiang, Xu Chen +2
We present Vid2Avatar, a method to learn human avatars from monocular in-the-wild videos. Reconstructing humans that move naturally from monocular in-the-wild videos is difficult.…
InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds
Tianjian Jiang, Xu Chen, Jie Song +1
In this paper, we take a significant step towards real-world applicability of monocular neural avatar reconstruction by contributing InstantAvatar, a system that can reconstruct hu…
Fast-SNARF: A Fast Deformer for Articulated Neural Fields
Xu Chen, Tianjian Jiang, Jie Song +4
Neural fields have revolutionized the area of 3D reconstruction and novel view synthesis of rigid scenes. A key challenge in making such methods applicable to articulated objects,…
Fully Automated End-to-End Fake Audio Detection
Chenglong Wang, Jiangyan Yi, Jianhua Tao +6
The existing fake audio detection systems often rely on expert experience to design the acoustic features or manually design the hyperparameters of the network structure. However,…
PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence
Zijian Dong, Chen Guo, Jie Song +3
We present a novel method to learn Personalized Implicit Neural Avatars (PINA) from a short RGB-D sequence. This allows non-expert users to create a detailed and personalized virtu…
gDNA: Towards Generative Detailed Neural Avatars
Xu Chen, Tianjian Jiang, Jie Song +4
To make 3D human avatars widely available, we must be able to generate a variety of 3D virtual humans with varied identities and shapes in arbitrary poses. This task is challenging…
I M Avatar: Implicit Morphable Head Avatars from Videos
Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C. Bühler +3
Traditional 3D morphable face models (3DMMs) provide fine-grained control over expression but cannot easily capture geometric and appearance details. Neural volumetric representati…
Human Performance Capture from Monocular Video in the Wild
Chen Guo, Xu Chen, Jie Song +1
Capturing the dynamically deforming 3D shape of clothed human is essential for numerous applications, including VR/AR, autonomous driving, and human-computer interaction. Existing…
Render In-between: Motion Guided Video Synthesis for Action Interpolation
Hsuan-I Ho, Xu Chen, Jie Song +1
Upsampling videos of human activity is an interesting yet challenging task with many potential applications ranging from gaming to entertainment and sports broadcasting. The main d…
Shape-aware Multi-Person Pose Estimation from Multi-View Images
Zijian Dong, Jie Song, Xu Chen +2
In this paper we contribute a simple yet effective approach for estimating 3D poses of multiple people from multi-view images. Our proposed coarse-to-fine pipeline first aggregates…
SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes
Xu Chen, Yufeng Zheng, Michael J. Black +2
Neural implicit surface representations have emerged as a promising paradigm to capture 3D shapes in a continuous and resolution-independent manner. However, adapting them to artic…
Human Body Model Fitting by Learned Gradient Descent
Jie Song, Xu Chen, Otmar Hilliges
We propose a novel algorithm for the fitting of 3D human shape to images. Combining the accuracy and refinement capabilities of iterative gradient-based optimization techniques wit…
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis
Xu Chen, Zijian Dong, Jie Song +2
Many object pose estimation algorithms rely on the analysis-by-synthesis framework which requires explicit representations of individual object instances. In this paper we combine…
Unpaired Pose Guided Human Image Generation
Xu Chen, Jie Song, Otmar Hilliges
This paper studies the task of full generative modelling of realistic images of humans, guided only by coarse sketch of the pose, while providing control over the specific instance…
Monocular Neural Image Based Rendering with Continuous View Control
Xu Chen, Jie Song, Otmar Hilliges
We present an approach that learns to synthesize high-quality, novel views of 3D objects or scenes, while providing fine-grained and precise control over the 6-DOF viewpoint. The a…
Fictitious GAN: Training GANs with Historical Models
Hao Ge, Yin Xia, Xu Chen +2
Generative adversarial networks (GANs) are powerful tools for learning generative models. In practice, the training may suffer from lack of convergence. GANs are commonly viewed as…