794 citations · 950 across the 14 of their papers we have counts for
14 papers
EditFlow3D: Automated Local Editing of 3D Assets with Trajectory Preservation
Rui Nie, Chuang Wang, Haitao Zhou +4
Controllable local editing of 3D assets requires precise target localization and appropriate visual guidance. However, existing methods lack a simple yet accurate way to obtain 3D…
AmodalSVG: Amodal Image Vectorization via Semantic Layer Peeling
Juncheng Hu, Ziteng Xue, Guotao Liang +5
We introduce AmodalSVG, a new framework for amodal image vectorization that produces semantically organized and geometrically complete SVG representations from natural images. Exis…
SegviGen: Repurposing 3D Generative Model for Part Segmentation
Lin Li, Haoran Feng, Zehuan Huang +8
We introduce SegviGen, a framework that repurposes native 3D generative models for 3D part segmentation. Existing pipelines either lift strong 2D priors into 3D via distillation or…
TEXTRIX: Latent Attribute Grid for Native Texture Generation and Beyond
Yifei Zeng, Yajie Bao, Jiachen Qian +7
Prevailing 3D texture generation methods, which often rely on multi-view fusion, are frequently hindered by inter-view inconsistencies and incomplete coverage of complex surfaces,…
SVGFusion: A VAE-Diffusion Transformer for Vector Graphic Generation
Ximing Xing, Juncheng Hu, Ziteng Xue +5
Generating high-quality Scalable Vector Graphics (SVGs) from text remains a significant challenge. Existing LLM-based models that generate SVG code as a flat token sequence struggl…
DanceFormer: Music Conditioned 3D Dance Generation with Parametric Motion Transformer
Buyu Li, Yongchi Zhao, Zhelun Shi +1
Generating 3D dances from music is an emerged research task that benefits a lot of applications in vision and graphics. Previous works treat this task as sequence generation, howev…
MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection
Xin Lu, Quanquan Li, Buyu Li +1
Modern object detection methods can be divided into one-stage approaches and two-stage ones. One-stage detectors are more efficient owing to straightforward architectures, but the…
Equalization Loss for Long-Tailed Object Recognition
Jingru Tan, Changbao Wang, Buyu Li +4
Object recognition techniques using convolutional neural networks (CNN) have achieved great success. However, state-of-the-art object detection methods still perform poorly on larg…
Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation
Yingjie Cai, Buyu Li, Zeyu Jiao +3
Monocular 3D object detection task aims to predict the 3D bounding boxes of objects based on monocular RGB images. Since the location recovery in 3D space is quite difficult on acc…
MMDetection: Open MMLab Detection Toolbox and Benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang +22
We present MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules. The to…
Grid R-CNN Plus: Faster and Better
Xin Lu, Buyu Li, Yuxin Yue +2
Grid R-CNN is a well-performed objection detection framework. It transforms the traditional box offset regression problem into a grid point estimation problem. With the guidance of…
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
Buyu Li, Wanli Ouyang, Lu Sheng +2
We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving. Our efforts are put on extracting the underlying 3D informa…
Grid R-CNN
Xin Lu, Buyu Li, Yuxin Yue +2
This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditi…
Gradient Harmonized Single-stage Detector
Buyu Li, Yu Liu, Xiaogang Wang
Despite the great success of two-stage detectors, single-stage detector is still a more elegant and efficient way, yet suffers from the two well-known disharmonies during training,…