9 citations · 12 across the 27 of their papers we have counts for
7 papers · 1 filter
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…
UrbanGS: Semantic-Guided Gaussian Splatting for Urban Scene Reconstruction
Ziwen Li, Jiaxin Huang, Runnan Chen +5
Reconstructing urban scenes is challenging due to their complex geometries and the presence of potentially dynamic objects. 3D Gaussian Splatting (3DGS)-based methods have shown st…
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…