Author name cluster

Rui Jiang

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

UniScene-MoTion: Unified Scene & Motion-aware Diffusion Transition Framework

Rui Jiang
Chongmian Wang
Xinghe Fu
Yehao Lu
Teng Li
Xi Li

Video transitions are critical for ensuring temporal coherence in edited media, yet existing methods often rely on handcrafted effects or relative-scale trajectories that fail to capture the physical structure of real-world scenes. In this work, we introduce a scale-aware video transition framework that explicitly incorporates depth-aware 3D reasoning into a diffusion-based generation pipeline. Built upon a powerful I2V foundation, our method leverages single-image depth prediction to align camera motion with metric-scale geometry, enabling physically consistent transitions. To reduce reliance on precise camera inputs, we propose a bidirectional conditional control module and a progressive training strategy with conditional dropout, enhancing generalization to loosely specified or missing camera trajectories. Extensive experiments demonstrate that our approach achieves state-of-the-art performance, delivering realistic, geometrically coherent transitions across diverse scenes and applications with minimal input guidance.

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EAAI Journal 2025 Journal Article

An image segmentation method for solid-liquid separation on shale shaker based on an improved U2Net

Wenbin Wang
Yongjun Hou
Rui Jiang
Pan Fang
Hong Peng
Qing Li
Huachuan Li

In the actual production process of shale shakers, detecting the solid-liquid separation state of the screen surface faces numerous challenges, such as difficulty in recognizing the mud boundary, insufficient anti-interference ability, and misjudgment caused by background interference. To address these issues, this paper proposes a screen surface mud image segmentation method based on U2Net, namely CBAM-U2Net. By introducing the Convolutional Block Attention Module (CBAM) and combining it with Multi-layer Recursive Residual Blocks (RSU), a network structure is designed that can efficiently fuse global and local features, significantly improving segmentation accuracy and robustness. The network includes encoder and decoder parts, employing convolution, batch normalization, ReLU activation, and multi-scale feature fusion strategies. Experimental results show that the CBAM-U2Net method demonstrates excellent segmentation performance under various working conditions, achieving outstanding results with mIoU, F1-score, Precision, and Recall at 83. 38%, 89. 75%, 89. 38%, and 92. 64%, respectively, with significantly enhanced anti-interference capability. The CBAM-U2Net method provides an efficient and reliable solution for the intelligent monitoring of the solid-liquid separation state in shale shakers, offering significant practical application value.

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IROS Conference 2025 Conference Paper

Design and Development of a Propulsion Induced Rolling Spherical Tensegrity Robot

Niansong Zhang
Rui Jiang
Xinfeng Shao
Yongliang Wu
Guiyan Qiang
Wenkai Huang
Yixiang Liu
Yibin Li

Spherical tensegrity structure has good dynamic stability, support strength and flexibility, and is widely used in the field of mobile robot research. Most of the tensegrity spherical robots deform themselves to make gravity work to realize the motion, but the deformation of both rods and ropes affects the robot's motion efficiency and motion instability. In this paper, a new type of tensegrity spherical robot is proposed, which is powered by six fixed ducted thrusters, and the thrust is provided to induce the robot to roll when in different attitudes. This paper firstly introduces the structural design and principle of the robot. Secondly analyzes the magnitude of the propulsive force required for the robot's motion and establishes a kinematic model. Finally, the robot prototype model was built and the robot motion experiments were conducted in simulation and the real environment respectively. The experimental results show that the robot has a simple structure but high motion efficiency, and has a strong ability to adapt to the environment.

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AAAI Conference 2025 Conference Paper

Energy-Guided Optimization for Personalized Image Editing with Pretrained Text-to-Image Diffusion Models

Rui Jiang
Xinghe Fu
Guangcong Zheng
Teng Li
Taiping Yao
Xi Li

The rapid advancement of pretrained text-driven diffusion models has significantly enriched applications in image generation and editing. However, as the demand for personalized content editing increases, new challenges emerge especially when dealing with arbitrary objects and complex scenes. Existing methods usually mistakes mask as the object shape prior, which struggle to achieve a seamless integration result. The mostly used inversion noise initialization also hinders the identity consistency towards the target object. To address these challenges, we propose a novel training-free framework that formulates personalized content editing as the optimization of edited images in the latent space, using diffusion models as the energy function guidance conditioned by reference text-image pairs. A coarse-to-fine strategy is proposed that employs text energy guidance at the early stage to achieve a natural transition toward the target class and uses point-to-point feature-level image energy guidance to perform fine-grained appearance alignment with the target object. Additionally, we introduce the latent space content composition to enhance overall identity consistency with the target. Extensive experiments demonstrate that our method excels in object replacement even with a large domain gap, highlighting its potential for high-quality, personalized image editing.

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IS Journal 2020 Journal Article

General Learning Modeling for AUV Position Tracking

Jia Guo
Rui Jiang
Bo He
Tianhong Yan
Shuzhi Sam Ge

In this article, we propose a nonlinear model based on hidden-layer neural networks and local Gaussian process regression. The hidden-layer neural networks have short training time, whereas the local Gaussian process regression is more suitable for nonlinearity. According to the abovementioned advantages of hidden-layer neural network and local Gaussian process regression, we get the measurement learning model and global process learning model offline and local process learning model online. Then, the proposed learning-based models have been applied to extended Kalman filter-simultaneous localization and mapping (EKF-SLAM), Rao-Blackwellised particle filter formulation of simultaneous localisation and mapping (FastSLAM), and unscented Kalman filter--simultaneous localization and mapping (UKF-SLAM) for autonomous underwater vehicles position tracking with field experiments. Compared with the conventional process and measurement models, improved position tracking performance can be achieved without cumbersome system analysis nor identification, benefiting from the proposed model. Experiments in more than 3883-m run show that the root-mean-square error has been improved by 36. 95%, 37. 14%, and 27. 65% in EKF-SLAM, FastSLAM, and UKF-SLAM frameworks, respectively.

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IROS Conference 2018 Conference Paper

Airborne Docking for Multi-Rotor Aerial Manipulations

Ryo Miyazaki
Rui Jiang
Hannibal Paul
Koji Ono
Kazuhiro Shimonomura

We have proposed airborne docking using two multi-rotor aerial robots. This paper presents a transport multi-rotor UAV with winch mechanism and a small multi-rotor with onboard locolization and mobile manipulation system. The winch mechanism enables the UAV to lower and raise a bar to transport another UAV attached to it. The airborne docking method used in our work is chosen in order to avoid the effect of downwash generated by the multi-rotors. With experiments we have verified the possibility of airborne docking, and evaluated how it influences the transport multi-rotor UAV as the load is changed, using the IMU data of UAV.

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