3 citations · 12 across the 16 of their papers we have counts for
14 papers · 1 filter
OneWorld: Taming Scene Generation with 3D Unified Representation Autoencoder
Sensen Gao, Zhaoqing Wang, Qihang Cao +5
Existing diffusion-based 3D scene generation methods primarily operate in 2D image/video latent spaces, which makes maintaining cross-view appearance and geometric consistency inhe…
MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input
Zhenchen Wan, Yanwu xu, Dongting Hu +6
Recent advancements in Virtual Try-On (VITON) have significantly improved image realism and garment detail preservation, driven by powerful text-to-image (T2I) diffusion models. Ho…
PanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAM
Runnan Chen, Zhaoqing Wang, Jiepeng Wang +4
Understanding geometric, semantic, and instance information in 3D scenes from sequential video data is essential for applications in robotics and augmented reality. However, existi…
OVGaussian: Generalizable 3D Gaussian Segmentation with Open Vocabularies
Runnan Chen, Xiangyu Sun, Zhaoqing Wang +8
Open-vocabulary scene understanding using 3D Gaussian (3DGS) representations has garnered considerable attention. However, existing methods mostly lift knowledge from large 2D visi…
Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels
Haodong Chen, Runnan Chen, Qiang Qu +4
Recent advancements in 3D Gaussian Splatting (3DGS) have substantially improved novel view synthesis, enabling high-quality reconstruction and real-time rendering. However, blurrin…
TED-VITON: Transformer-Empowered Diffusion Models for Virtual Try-On
Zhenchen Wan, Yanwu Xu, Zhaoqing Wang +3
Recent advancements in Virtual Try-On (VTO) have demonstrated exceptional efficacy in generating realistic images and preserving garment details, largely attributed to the robust g…
LaVin-DiT: Large Vision Diffusion Transformer
Zhaoqing Wang, Xiaobo Xia, Runnan Chen +4
This paper presents the Large Vision Diffusion Transformer (LaVin-DiT), a scalable and unified foundation model designed to tackle over 20 computer vision tasks in a generative fra…
Training-Free Robust Interactive Video Object Segmentation
Xiaoli Wei, Zhaoqing Wang, Yandong Guo +3
Interactive video object segmentation is a crucial video task, having various applications from video editing to data annotating. However, current approaches struggle to accurately…
Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision
Zhaoqing Wang, Xiaobo Xia, Ziye Chen +4
Current state-of-the-art open-vocabulary segmentation methods typically rely on image-mask-text triplet annotations for supervision. However, acquiring such detailed annotations is…
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks
Xiaowei Chi, Jiaming Liu, Ming Lu +4
Bird's-Eye-View (BEV) 3D Object Detection is a crucial multi-view technique for autonomous driving systems. Recently, plenty of works are proposed, following a similar paradigm con…
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view Stereo
Shiyu Gao, Zhaoxin Li, Zhaoqi Wang
Multi-view stereo is an important research task in computer vision while still keeping challenging. In recent years, deep learning-based methods have shown superior performance on…
MSR: Making Self-supervised learning Robust to Aggressive Augmentations
Yingbin Bai, Erkun Yang, Zhaoqing Wang +5
Most recent self-supervised learning methods learn visual representation by contrasting different augmented views of images. Compared with supervised learning, more aggressive augm…
CRIS: CLIP-Driven Referring Image Segmentation
Zhaoqing Wang, Yu Lu, Qiang Li +4
Referring image segmentation aims to segment a referent via a natural linguistic expression.Due to the distinct data properties between text and image, it is challenging for a netw…
Exploring Set Similarity for Dense Self-supervised Representation Learning
Zhaoqing Wang, Qiang Li, Guoxin Zhang +5
By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks. However, the pixel-lev…