15 citations · 68 across the 11 of their papers we have counts for
11 papers
DreamOmni3: Scribble-based Editing and Generation
Bin Xia, Bohao Peng, Jiyang Liu +8
Recently unified generation and editing models have achieved remarkable success with their impressive performance. These models rely mainly on text prompts for instruction-based ed…
DreamVE: Unified Instruction-based Image and Video Editing
Bin Xia, Jiyang Liu, Yuechen Zhang +6
Instruction-based editing holds vast potential due to its simple and efficient interactive editing format. However, instruction-based editing, particularly for video, has been cons…
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Yanwei Li, Yuechen Zhang, Chengyao Wang +5
In this work, we introduce Mini-Gemini, a simple and effective framework enhancing multi-modality Vision Language Models (VLMs). Despite the advancements in VLMs facilitating basic…
Mask-Attention-Free Transformer for 3D Instance Segmentation
Xin Lai, Yuhui Yuan, Ruihang Chu +3
Recently, transformer-based methods have dominated 3D instance segmentation, where mask attention is commonly involved. Specifically, object queries are guided by the initial insta…
DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation
Shentong Mo, Enze Xie, Ruihang Chu +4
Recent Diffusion Transformers (e.g., DiT) have demonstrated their powerful effectiveness in generating high-quality 2D images. However, it is still being determined whether the Tra…
DiffComplete: Diffusion-based Generative 3D Shape Completion
Ruihang Chu, Enze Xie, Shentong Mo +4
We introduce a new diffusion-based approach for shape completion on 3D range scans. Compared with prior deterministic and probabilistic methods, we strike a balance between realism…
TriVol: Point Cloud Rendering via Triple Volumes
Tao Hu, Xiaogang Xu, Ruihang Chu +1
Existing learning-based methods for point cloud rendering adopt various 3D representations and feature querying mechanisms to alleviate the sparsity problem of point clouds. Howeve…
Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation
Yiming Li, Tao Kong, Ruihang Chu +3
Grasping in cluttered scenes has always been a great challenge for robots, due to the requirement of the ability to well understand the scene and object information. Previous works…
ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation
Ruihang Chu, Yukang Chen, Tao Kong +2
Separating 3D point clouds into individual instances is an important task for 3D vision. It is challenging due to the unknown and varying number of instances in a scene. Existing d…
Scale-aware Automatic Augmentation for Object Detection
Yukang Chen, Yanwei Li, Tao Kong +4
We propose Scale-aware AutoAug to learn data augmentation policies for object detection. We define a new scale-aware search space, where both image- and box-level augmentations are…
Vehicle Re-identification with Viewpoint-aware Metric Learning
Ruihang Chu, Yifan Sun, Yadong Li +3
This paper considers vehicle re-identification (re-ID) problem. The extreme viewpoint variation (up to 180 degrees) poses great challenges for existing approaches. Inspired by the…