18 citations · 46 across the 10 of their papers we have counts for
10 papers
Any3D-VLA: Enhancing VLA Robustness via Diverse Point Clouds
Xianzhe Fan, Shengliang Deng, Xiaoyang Wu +7
Existing Vision-Language-Action (VLA) models typically take 2D images as visual input, which limits their spatial understanding in complex scenes. How can we incorporate 3D informa…
Sim-to-Real Dynamic Object Manipulation on Conveyor Systems via Optimization Path Shaping
Zhuoling Li, Jinrong Yang, Yong Zhao +4
Realizing generalizable dynamic object manipulation on conveyor systems is important for enhancing manufacturing efficiency, as it eliminates specialized engineering for different…
DreamComposer++: Empowering Diffusion Models with Multi-View Conditions for 3D Content Generation
Yunhan Yang, Shuo Chen, Yukun Huang +6
Recent advancements in leveraging pre-trained 2D diffusion models achieve the generation of high-quality novel views from a single in-the-wild image. However, existing works face c…
UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation
Lihe Yang, Zhen Zhao, Hengshuang Zhao
Semi-supervised semantic segmentation (SSS) aims at learning rich visual knowledge from cheap unlabeled images to enhance semantic segmentation capability. Among recent works, UniM…
VIP: Vision Instructed Pre-training for Robotic Manipulation
Zhuoling Li, Liangliang Ren, Jinrong Yang +5
The effectiveness of scaling up training data in robotic manipulation is still limited. A primary challenge in manipulation is the tasks are diverse, and the trained policy would b…
EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions
Kai Chen, Yunhao Gou, Runhui Huang +28
GPT-4o, an omni-modal model that enables vocal conversations with diverse emotions and tones, marks a milestone for omni-modal foundation models. However, empowering Large Language…
Depth Anything V2
Lihe Yang, Bingyi Kang, Zilong Huang +4
This work presents Depth Anything V2. Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation mo…
LARM: Large Auto-Regressive Model for Long-Horizon Embodied Intelligence
Zhuoling Li, Xiaogang Xu, Zhenhua Xu +2
Recent embodied agents are primarily built based on reinforcement learning (RL) or large language models (LLMs). Among them, RL agents are efficient for deployment but only perform…
DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model
Zhenhua Xu, Yujia Zhang, Enze Xie +5
Multimodal large language models (MLLMs) have emerged as a prominent area of interest within the research community, given their proficiency in handling and reasoning with non-text…
LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields
Tang Tao, Longfei Gao, Guangrun Wang +7
We introduce a new task, novel view synthesis for LiDAR sensors. While traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel v…