Yanwei Fu

OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation

Kuanning Wang, Ke Fan, Chenhao Qiu +5

Robust robotic manipulation requires not only predicting how the scene evolves over time, but also recognizing task-relevant objects in complex scenes. However, existing VLA models…

Mind-of-Director: Multi-modal Agent-Driven Film Previsualization via Collaborative Decision-Making

Shufeng Nan, Mengtian Li, Sixiao Zheng +3

We present Mind-of-Director, a multi-modal agent-driven framework for film previz that models the collaborative decision-making process of a film production team. Given a creative…

ST4VLA: Spatially Guided Training for Vision-Language-Action Models

Jinhui Ye, Fangjing Wang, Ning Gao +9

Large vision-language models (VLMs) excel at multimodal understanding but fall short when extended to embodied tasks, where instructions must be transformed into low-level motor ac…

Universal Pose Pretraining for Generalizable Vision-Language-Action Policies

Haitao Lin, Hanyang Yu, Jingshun Huang +5

Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low training efficiency because they entangle high-level perception with sparse, embodiment-spec…

ActiveVLA: Injecting Active Perception into Vision-Language-Action Models for Precise 3D Robotic Manipulation

Zhenyang Liu, Yongchong Gu, Yikai Wang +2

Recent advances in robot manipulation have leveraged pre-trained vision-language models (VLMs) and explored integrating 3D spatial signals into these models for effective action pr…

DST-Calib: A Dual-Path, Self-Supervised, Target-Free LiDAR-Camera Extrinsic Calibration Network

Zhiwei Huang, Yanwei Fu, Yi Zhou +3

LiDAR-camera extrinsic calibration is essential for multi-modal data fusion in robotic perception systems. However, existing approaches typically rely on handcrafted calibration ta…

MVGGT: Multimodal Visual Geometry Grounded Transformer for Multiview 3D Referring Expression Segmentation

Changli Wu, Haodong Wang, Jiayi Ji +5

Most existing 3D referring expression segmentation (3DRES) methods rely on dense, high-quality point clouds, while real-world agents such as robots and mobile phones operate with o…

VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control

Sixiao Zheng, Minghao Yin, Wenbo Hu +3

Video world models aim to simulate dynamic, real-world environments, yet existing methods struggle to provide unified and precise control over camera and multi-object motion, as vi…

ReVSeg: Incentivizing the Reasoning Chain for Video Segmentation with Reinforcement Learning

Yifan Li, Yingda Yin, Lingting Zhu +4

Reasoning-centric video object segmentation is an inherently complex task: the query often refers to dynamics, causality, and temporal interactions, rather than static appearances.…

Schrödinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation

Yu He, Da Huang, Zhenyang Liu +5

Zero-shot object navigation (ZSON) requires robots to find target objects in unseen environments without task-specific fine-tuning or pre-built maps, a key capability for general-p…

TP-MDDN: Task-Preferenced Multi-Demand-Driven Navigation with Autonomous Decision-Making

Shanshan Li, Da Huang, Yu He +3

In daily life, people often move through spaces to find objects that meet their needs, posing a key challenge in embodied AI. Traditional Demand-Driven Navigation (DDN) handles one…

Enhancing Video Inpainting with Aligned Frame Interval Guidance

Ming Xie, Junqiu Yu, Qiaole Dong +2

Recent image-to-video (I2V) based video inpainting methods have made significant strides by leveraging single-image priors and modeling temporal consistency across masked frames. N…

Beyond 'Templates': Category-Agnostic Object Pose, Size, and Shape Estimation from a Single View

Jinyu Zhang, Haitao Lin, Jiashu Hou +2

Estimating an object's 6D pose, size, and shape from visual input is a fundamental problem in computer vision, with critical applications in robotic grasping and manipulation. Exis…

Diffusion-Based Imaginative Coordination for Bimanual Manipulation

Huilin Xu, Jian Ding, Jiakun Xu +7

Bimanual manipulation is crucial in robotics, enabling complex tasks in industrial automation and household services. However, it poses significant challenges due to the high-dimen…

Spatial-Temporal Aware Visuomotor Diffusion Policy Learning

Zhenyang Liu, Yikai Wang, Kuanning Wang +3

Visual imitation learning is effective for robots to learn versatile tasks. However, many existing methods rely on behavior cloning with supervised historical trajectories, limitin…

A Neural Representation Framework with LLM-Driven Spatial Reasoning for Open-Vocabulary 3D Visual Grounding

Zhenyang Liu, Sixiao Zheng, Siyu Chen +4

Open-vocabulary 3D visual grounding aims to localize target objects based on free-form language queries, which is crucial for embodied AI applications such as autonomous navigation…

TriVLA: A Triple-System-Based Unified Vision-Language-Action Model with Episodic World Modeling for General Robot Control

Zhenyang Liu, Yongchong Gu, Sixiao Zheng +3

Recent advances in vision-language models (VLMs) have enabled robots to follow open-ended instructions and demonstrate impressive commonsense reasoning. However, current vision-lan…