Hao-Tang Tsui

Bo-Kai Ruan, Hao-Tang Tsui, Yung-Hui Li et al.

Generating realistic and controllable traffic scenes from natural language can greatly enhance the development and evaluation of autonomous driving systems. However, this task poses unique challenges: (1) grounding free-form text into spatially valid and semantically coherent layouts, (2) composing scenarios without predefined locations, and (3) planning multi-agent behaviors and selecting roads that respect agents' configurations. To address these, we propose a modular framework, TTSG, comprising prompt analysis, road retrieval, agent planning, and a novel plan-aware road ranking algorithm to solve these challenges. While large language models (LLMs) are used as general planners, our design integrates them into a tightly controlled pipeline that enforces structure, feasibility, and scene diversity. Notably, our ranking strategy ensures consistency between agent actions and road geometry, enabling scene generation without predefined routes or spawn points. The framework supports both routine and safety-critical scenarios, as well as multi-stage event composition. Experiments on SafeBench demonstrate that our method achieves the lowest average collision rate (3.5\%) across three critical scenarios. Moreover, driving captioning models trained on our generated scenes improve action reasoning by over 30 CIDEr points. These results underscore our proposed framework for flexible, interpretable, and safety-oriented simulation.

Hao-Tang Tsui, Chien-Yao Wang, Hong-Yuan Mark Liao

Identifying and localizing objects within images is a fundamental challenge, and numerous efforts have been made to enhance model accuracy by experimenting with diverse architectures and refining training strategies. Nevertheless, a prevalent limitation in existing models is overemphasizing the current input while ignoring the information from the entire dataset. We introduce an innovative Retriever-Dictionary (RD) module to address this issue. This architecture enables YOLO-based models to efficiently retrieve features from a Dictionary that contains the insight of the dataset, which is built by the knowledge from Visual Models (VM), Large Language Models (LLM), or Visual Language Models (VLM). The flexible RD enables the model to incorporate such explicit knowledge that enhances the ability to benefit multiple tasks, specifically, segmentation, detection, and classification, from pixel to image level. The experiments show that using the RD significantly improves model performance, achieving more than a 3\% increase in mean Average Precision for object detection with less than a 1% increase in model parameters. Beyond 1-stage object detection models, the RD module improves the effectiveness of 2-stage models and DETR-based architectures, such as Faster R-CNN and Deformable DETR. Code is released at https://github.com/henrytsui000/YOLO.

Hao-Tang Tsui, Yu-Rou Tuan, Hong-Han Shuai

It is a common problem in robotics to specify the position of each joint of the robot so that the endpoint reaches a certain target in space. This can be solved in two ways, forward kinematics method and inverse kinematics method. However, inverse kinematics cannot be solved by an algorithm. The common method is the Jacobian inverse technique, and some people have tried to find the answer by machine learning. In this project, we will show how to use the Conditional Denoising Diffusion Probabilistic Model to integrate the solution of calculating IK. Index Terms: Inverse kinematics, Denoising Diffusion Probabilistic Model, self Attention, Transformer

Hao-Tang Tsui, Yu-Rou Tuan, Jia-You Chen

This work is a portable MetaVerse implementation, and we use 3D pose estimation with AI to make virtual avatars do synchronized actions and interact with the environment. The motivation is that we find it inconvenient to use joysticks and sensors when playing with fitness rings. In order to replace joysticks and reduce costs, we developed a platform that can control virtual avatars through pose estimation to identify the movements of real people, and we also implemented a multi-process to achieve modularization and reduce the overall latency.