Author name cluster

Guoqing Yang

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

OneFont: A Unified Agent for End-to-End Font Creation

Yingxin Lai
Yufei Liu
Guoqing Yang
Jiaxing Chai
Zhiming Luo
Shaozi Li

Despite recent advancements in font generation, practitioners still grapple with a laborious trial-and-error workflow. To streamline this, we propose OneFont, an end-to-end framework that interprets user intents via free-form dialogue, seamlessly integrating both glyph synthesis and refinement modules. We introduce the Font with Thought (FwT) paradigm, reframing font design as a reasoning task where the model plans actions and articulates design rationales. OneFont’s core planner is trained via a two-stage regimen to master this paradigm. First, we instill reasoning abilities via Supervised Fine-Tuning (SFT) on a new, comprehensive benchmark of 1,500 font families we built. Second, we refine the model's policy with a novel reinforcement learning algorithm, Group Relative Policy Optimization (GRPO), guided by a hybrid reward that assesses visual fidelity, rationale coherence, and transformation correctness. Extensive experiments show OneFont significantly surpasses existing methods in design quality and stroke precision across diverse scripts, validated on our new benchmark. We will release our dataset, code, and models.

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ECAI Conference 2024 Conference Paper

Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformers

Mengke Li 0001
Da Li
Guoqing Yang
Yiu-ming Cheung
Hui Huang 0004

Pre-trained large-scale models have exhibited remarkable efficacy in computer vision, particularly for 2D image analysis. However, when it comes to 3D point clouds, the constrained accessibility of data, in contrast to the vast repositories of images, poses a challenge for the development of 3D pre-trained models. This paper therefore attempts to directly leverage pre-trained models with 2D prior knowledge to accomplish the tasks for 3D point cloud analysis. Accordingly, we propose the Adaptive PointFormer (APF), which fine-tunes pre-trained 2D models with only a modest number of parameters to directly process point clouds, obviating the need for mapping to images. Specifically, we convert raw point clouds into point embeddings for aligning dimensions with image tokens. Given the inherent disorder in point clouds, in contrast to the structured nature of images, we then sequence the point embeddings to optimize the utilization of 2D attention priors. To calibrate attention across 3D and 2D domains and reduce computational overhead, a trainable PointFormer with a limited number of parameters is subsequently concatenated to a frozen pre-trained image model. Extensive experiments on various benchmarks demonstrate the effectiveness of the proposed APF. The source code and more details are available at https: //vcc. tech/research/2024/PointFormer.

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IROS Conference 2024 Conference Paper

SmartKit: User-Friendly Robot with Multiple Operating Systems

Guanyu Chen
Yiqun Zhou
Guoqing Yang
Hong Li
Pan Lv

Mobile robots have become extensively involved in human activities, taking on arduous tasks and providing significant assistance. Robot capabilities have been continuously enhanced, from simple chassis control to path planning and SLAM. Mixed criticality systems enable mobile robots to handle tasks of varying criticality by integrating multiple operating systems, allowing them to accomplish a wide range of tasks. However, besides improving robot computing performance, we should remember that robots are designed to serve humans. Reliability, usability, and affordability are all critical factors for robot design. We introduce SmartKit, a mixed criticality system (MCS) for mobile robots. Leveraging the efficiency in hardware utilization brought by virtualization, SmartKit can execute tasks of different criticality efficiently and securely. This paper will present the software and hardware architecture of SmartKit and provide performance and functionality validation of the robot system.

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