Author name cluster

Junjie Cao

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

STMI: Segmentation-Guided Token Modulation with Cross-Modal Hypergraph Interaction for Multi-Modal Object Re-Identification

Xingguo Xu
Zhanyu Liu
Weixiang Zhou
Yuansheng Gao
Junjie Cao
Yuhao Wang
Jixiang Luo
Dell Zhang

Multi-modal object Re-Identification (ReID) aims to exploit complementary information from different modalities to retrieve specific objects. However, existing methods often rely on hard token filtering or simple fusion strategies, which can lead to the loss of discriminative cues and increased background interference. To address these challenges, we propose STMI, a novel multi-modal learning framework consisting of three key components: (1) Segmentation-Guided Feature Modulation (SFM) module leverages SAM-generated masks to enhance foreground representations and suppress background noise through learnable attention modulation; (2) Semantic Token Reallocation (STR) module employs learnable query tokens and an adaptive reallocation mechanism to extract compact and informative representations without discarding any tokens; (3) Cross-Modal Hypergraph Interaction (CHI) module constructs a unified hypergraph across modalities to capture high-order semantic relationships. Extensive experiments on public benchmarks (i.e., RGBNT201, RGBNT100, and MSVR310) demonstrate the effectiveness and robustness of our proposed STMI framework in multi-modal ReID scenarios.

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ICLR Conference 2025 Conference Paper

DisPose: Disentangling Pose Guidance for Controllable Human Image Animation

Hongxiang Li 0004
Yaowei Li 0001
Yuhang Yang
Junjie Cao
Zhihong Zhu
Xuxin Cheng
Long Chen 0016

Controllable human image animation aims to generate videos from reference images using driving videos. Due to the limited control signals provided by sparse guidance (e.g., skeleton pose), recent works have attempted to introduce additional dense conditions (e.g., depth map) to ensure motion alignment. However, such strict dense guidance impairs the quality of the generated video when the body shape of the reference character differs significantly from that of the driving video. In this paper, we present DisPose to mine more generalizable and effective control signals without additional dense input, which disentangles the sparse skeleton pose in human image animation into motion field guidance and keypoint correspondence. Specifically, we generate a dense motion field from a sparse motion field and the reference image, which provides region-level dense guidance while maintaining the generalization of the sparse pose control. We also extract diffusion features corresponding to pose keypoints from the reference image, and then these point features are transferred to the target pose to provide distinct identity information. To seamlessly integrate into existing models, we propose a plug-and-play hybrid ControlNet that improves the quality and consistency of generated videos while freezing the existing model parameters. Extensive qualitative and quantitative experiments demonstrate the superiority of DisPose compared to current methods. Project page: https://github.com/lihxxx/DisPose.

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JBHI Journal 2025 Journal Article

MSTG-Transformer: Multivariate Spatial-Temporal Gated Transformer Model for 3D Skeleton Data-based Fall Risk Prediction

Junjie Cao
Xuan Wang
Keyi Huang
Lisha Yu
Xiaomao Fan
Yang Zhao

As the aging population continues to grow, falls among older adults have become a significant public health concern worldwide. Data-driven approaches for effective fall risk prediction, which integrate standard functional tests with 3D skeleton data from depth sensors, are gaining increasing attention. However, the complex physiological and functional interactions among skeletal keypoints during ambulation pose challenges for multidimensional feature extraction in most predictive models. In this study, we developed a novel approach based on preprocessed 3D skeleton data, named Multivariate SpatialTemporal Gated Transformer (MSTG-Transformer). This approach consists of three main stages. First, gait cycle sequences are constructed to sophisticatedly depict the movement patterns of subjects, amplifying the distinctions between groups. Then, spatial and topological features are extracted via convolutional modules, and a dual-stream encoder block is employed to encode the features of 3D skeleton data across both time steps and time channels. Finally, a voting scheme is used to determine fall risk by integrating the classification results of individual gait cycle segments. Validation experiments on a real-world dataset demonstrate that our proposed approach outperforms classical methods, achieving a superior prediction accuracy of 0. 9510 ± 0. 0240. Additionally, our study highlights the crucial role of potential interactions between skeletal keypoints in accurately predicting fall risk

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IROS Conference 2024 Conference Paper

Hierarchical Search-Based Cooperative Motion Planning

Yuchen Wu
Yifan Yang
Gang Xu
Junjie Cao
Yansong Chen
Licheng Wen
Yong Liu 0007

Cooperative path planning, a crucial aspect of multi-agent systems research, serves a variety of sectors, including military, agriculture, and industry. Many existing algorithms, however, come with certain limitations, such as simplified kinematic models and inadequate support for multiple group scenarios. Focusing on the planning problem associated with a nonholonomic Ackermann model for Unmanned Ground Vehicles (UGV), we propose a leaderless, hierarchical Search-Based Cooperative Motion Planning (SCMP) method. The high-level utilizes a binary conflict search tree to minimize runtime, while the low-level fabricates kinematically feasible, collision-free paths that are shape-constrained. Our algorithm can adapt to scenarios featuring multiple groups with different shapes, outlier agents, and elaborate obstacles. We conduct algorithm comparisons, performance testing, simulation, and real-world testing, verifying the effectiveness and applicability of our algorithm. The implementation of our method will be open-sourced at https://github.com/WYCUniverStar/SCMP.

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IROS Conference 2023 Conference Paper

Large Scale Pursuit-Evasion Under Collision Avoidance Using Deep Reinforcement Learning

Helei Yang
Peng Ge
Junjie Cao
Yifan Yang
Yong Liu 0007

This paper examines a pursuit-evasion game (PEG) involving multiple pursuers and evaders. The decentralized pursuers aim to collaborate to capture the faster evaders while avoiding collisions. The policies of all agents are learning-based and are subjected to kinematic constraints that are specific to unicycles. To address the challenge of high dimensionality encountered in large-scale scenarios, we propose a state processing method named Mix-Attention, which is based on Self-Attention. This method effectively mitigates the curse of dimensionality. The simulation results provided in this study demonstrate that the combination of Mix-Attention and Independent Proximal Policy Optimization (IPPO) surpasses alternative approaches when solving the multi-pursuer multi-evader PEG, particularly as the number of entities increases. Moreover, the trained policies showcase their ability to adapt to scenarios involving varying numbers of agents and obstacles without requiring retraining. This adaptability showcases their transferability and robustness. Finally, our proposed approach has been validated through physical experiments conducted with six robots.

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ICRA Conference 2023 Conference Paper

Shunted Collision Avoidance for Multi-UAV Motion Planning with Posture Constraints

Gang Xu
Deye Zhu
Junjie Cao
Yong Liu 0007
Jian Yang 0003

This paper investigates the problem of fixed-wing unmanned aerial vehicles (UAV s) motion planning with posture constraints and the problem of the more general symmetrical situations where UAVs have more than one optimal solution. In this paper, the posture constraints are formulated in the 3D Dubins method, and the symmetrical situations are overcome by a more collaborative strategy called the shunted strategy. The effectiveness of the proposed method has been validated by conducting extensive simulation experiments. Meanwhile, we compared the proposed method with the other state-of-the-art methods, and the comparison results show that the proposed method advances the previous works. Finally, the practicability of the proposed algorithm was analyzed by the statistic in computational cost. The source code of our method can be available at https://github.com/wuuya1/SCA.

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IROS Conference 2021 Conference Paper

Moving Forward in Formation: A Decentralized Hierarchical Learning Approach to Multi-Agent Moving Together

Shanqi Liu
Licheng Wen
Jinhao Cui
Xuemeng Yang
Junjie Cao
Yong Liu 0007

Multi-agent path finding in formation has many potential real-world applications like mobile warehouse robotics. However, previous multi-agent path finding (MAPF) methods hardly take formation into consideration. Further-more, they are usually centralized planners and require the whole state of the environment. Other decentralized partially observable approaches to MAPF are reinforcement learning (RL) methods. However, these RL methods encounter difficulties when learning path finding and formation problems at the same time. In this paper, we propose a novel decentralized partially observable RL algorithm that uses a hierarchical structure to decompose the multi-objective task into unrelated ones. It also calculates a theoretical weight that makes each tasks reward has equal influence on the final RL value function. Additionally, we introduce a communication method that helps agents cooperate with each other. Experiments in simulation show that our method outperforms other end-to-end RL methods and our method can naturally scale to large world sizes where centralized planner struggles. We also deploy and validate our method in a real-world scenario.

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