1 citations · 1 across the 9 of their papers we have counts for
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Towards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Grounding
Jiahao Li, Qingwang Zhang, Qiuyu Chen +3
The field of Computer-Aided Design (CAD) generation has made significant progress in recent years. Existing methods typically fall into two separate categories: parametric CAD mode…
AutoRegressive Generation with B-rep Holistic Token Sequence Representation
Jiahao Li, Yunpeng Bai, Yongkang Dai +3
Previous representation and generation approaches for the B-rep relied on graph-based representations that disentangle geometric and topological features through decoupled computat…
Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition
Shengming Yin, Zekai Zhang, Zecheng Tang +11
Recent visual generative models often struggle with consistency during image editing due to the entangled nature of raster images, where all visual content is fused into a single c…
ReCAD: Reinforcement Learning Enhanced Parametric CAD Model Generation with Vision-Language Models
Jiahao Li, Yusheng Luo, Yunzhong Lou +1
We present ReCAD, a reinforcement learning (RL) framework that bootstraps pretrained large models (PLMs) to generate precise parametric computer-aided design (CAD) models from mult…
Global Regulation and Excitation via Attention Tuning for Stereo Matching
Jiahao Li, Xinhong Chen, Zhengmin Jiang +3
Stereo matching achieves significant progress with iterative algorithms like RAFT-Stereo and IGEV-Stereo. However, these methods struggle in ill-posed regions with occlusions, text…
CAD-Llama: Leveraging Large Language Models for Computer-Aided Design Parametric 3D Model Generation
Jiahao Li, Weijian Ma, Xueyang Li +3
Recently, Large Language Models (LLMs) have achieved significant success, prompting increased interest in expanding their generative capabilities beyond general text into domain-sp…