45 citations · 76 across the 16 of their papers we have counts for
16 papers
CasaGPT: Cuboid Arrangement and Scene Assembly for Interior Design
Weitao Feng, Hang Zhou, Jing Liao +2
We present a novel approach for indoor scene synthesis, which learns to arrange decomposed cuboid primitives to represent 3D objects within a scene. Unlike conventional methods tha…
BOOTPLACE: Bootstrapped Object Placement with Detection Transformers
Hang Zhou, Xinxin Zuo, Rui Ma +1
In this paper, we tackle the copy-paste image-to-image composition problem with a focus on object placement learning. Prior methods have leveraged generative models to reduce the r…
Forest2Seq: Revitalizing Order Prior for Sequential Indoor Scene Synthesis
Qi Sun, Hang Zhou, Wengang Zhou +2
Synthesizing realistic 3D indoor scenes is a challenging task that traditionally relies on manual arrangement and annotation by expert designers. Recent advances in autoregressive…
DAE-Net: Deforming Auto-Encoder for fine-grained shape co-segmentation
Zhiqin Chen, Qimin Chen, Hang Zhou +1
We present an unsupervised 3D shape co-segmentation method which learns a set of deformable part templates from a shape collection. To accommodate structural variations in the coll…
ShaDDR: Interactive Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering
Qimin Chen, Zhiqin Chen, Hang Zhou +1
We present ShaDDR, an example-based deep generative neural network which produces a high-resolution textured 3D shape through geometry detailization and conditional texture generat…
Building an Invisible Shield for Your Portrait against Deepfakes
Jiazhi Guan, Tianshu Hu, Hang Zhou +5
The issue of detecting deepfakes has garnered significant attention in the research community, with the goal of identifying facial manipulations for abuse prevention. Although rece…
Shape-invariant 3D Adversarial Point Clouds
Qidong Huang, Xiaoyi Dong, Dongdong Chen +3
Adversary and invisibility are two fundamental but conflict characters of adversarial perturbations. Previous adversarial attacks on 3D point cloud recognition have often been crit…
SAC-GAN: Structure-Aware Image Composition
Hang Zhou, Rui Ma, Ling-Xiao Zhang +3
We introduce an end-to-end learning framework for image-to-image composition, aiming to plausibly compose an object represented as a cropped patch from an object image into a backg…
From Image to Imuge: Immunized Image Generation
Qichao Ying, Zhenxing Qian, Hang Zhou +3
We introduce Imuge, an image tamper resilient generative scheme for image self-recovery. The traditional manner of concealing image content within the image are inflexible and frag…
Hiding Images into Images with Real-world Robustness
Qichao Ying, Hang Zhou, Xianhan Zeng +3
The existing image embedding networks are basically vulnerable to malicious attacks such as JPEG compression and noise adding, not applicable for real-world copyright protection ta…
Three-Dimensional Mesh Steganography and Steganalysis: A Review
Hang Zhou, Weiming Zhang, Kejiang Chen +2
Three-dimensional (3-D) meshes are commonly used to represent virtual surfaces and volumes. Over the past decade, 3-D meshes have emerged in industrial, medical, and entertainment…
Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication
Xiquan Guan, Huamin Feng, Weiming Zhang +3
Deep convolutional neural networks have made outstanding contributions in many fields such as computer vision in the past few years and many researchers published well-trained netw…
Adversarial Examples Detection beyond Image Space
Kejiang Chen, Yuefeng Chen, Hang Zhou +4
Deep neural networks have been proved that they are vulnerable to adversarial examples, which are generated by adding human-imperceptible perturbations to images. To defend these a…
LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks
Hang Zhou, Dongdong Chen, Jing Liao +6
Deep neural networks have made tremendous progress in 3D point-cloud recognition. Recent works have shown that these 3D recognition networks are also vulnerable to adversarial samp…
Self-supervised Adversarial Training
Kejiang Chen, Hang Zhou, Yuefeng Chen +6
Recent work has demonstrated that neural networks are vulnerable to adversarial examples. To escape from the predicament, many works try to harden the model in various ways, in whi…
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense
Hang Zhou, Kejiang Chen, Weiming Zhang +3
Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose a Denoiser and UPsampler Network (DUP-Ne…