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  • Text-to-CAD Generation Through Infusing Visual Feedback in Large . . .
    In this work, we introduce CADFusion, a framework that uses Large Language Models (LLMs) as the backbone and alternates between two training stages: the sequential learning (SL) stage and the visual feedback (VF) stage
  • Text-to-CAD Generation Through Infusing Visual Feedback in Large . . .
    We developed a text-to-CAD system on not only sequential learning but also visual feedback This paradigm improved the model performance by integrating CAD's visual quality into the pipeline This helps the system produce cleaner, more natural designs with smooth edges and correct holes
  • Text-to-CAD Generation Through Infusing Visual Feedback in Large . . .
    In this work, we introduce CADFusion, a framework that uses Large Language Models (LLMs) as the backbone and alternates between two training stages: the sequential learning (SL) stage and the visual feedback (VF) stage
  • Text-to-CAD Generation Through Infusing Visual Feedback in Large . . .
    Generating CAD from written instructions involve model training on only sequential data pairs, which posed problems on learning efficiency and final performance We developed a text-to-CAD system on not only sequential learning but also visual feedback
  • GitHub - microsoft CADFusion
    CADFusion is a text-to-CAD generation framework that leverages visual feedback to enhance the performance of large language models (LLMs) in generating CAD models from textual descriptions It consists of two main components: sequential learning and visual learning
  • Text-to-CAD generation through infusing visual feedback in large . . .
    Text-to-CAD generation through infusing visual feedback in large language models | Proceedings of the 42nd International Conference on Machine Learning
  • Text-to-CAD Generation Through Infusing Visual Feedback in Large . . .
    In this work, we introduce CADFusion, a framework that uses Large Language Models (LLMs) as the backbone and alternates between two training stages: the sequential learning (SL) stage and the
  • Text-to-CAD Generation Through Infusing Visual Feedback in Large . . .
    A novel text-to-CAD framework that leverages large language models (LLMs) and a B-Rep grounding transformer (BRepGround) for high-fidelity CAD generation, and achieves state-of-the-art CAD generation performance
  • Text-to-CAD Generation Through Infusing Visual Feedback in . . .
    论文试图解决将文本描述转换为计算机辅助设计（CAD）模型的问题，特别是通过利用大型语言模型（LLMs）来生成逻辑连贯的参数序列，并确保这些序列能渲染成视觉上令人满意的对象。 这是一个相对新颖的问题，因为传统的CAD建模需要大量的专业知识和时间，而文本到CAD的转换可以显著简化这一过程。 关键思路是引入了CADFusion框架，该框架结合了两个交替的训练阶段：顺序学习（SL）阶段和视觉反馈（VF）阶段。 在SL阶段，LLMs通过使用真实的参数序列进行训练，以生成逻辑连贯的参数序列；在VF阶段，则通过奖励或惩罚机制调整参数序列，以确保它们能够渲染出视觉上更符合预期的对象。 这种方法不仅利用了参数序列作为监督信号，还结合了视觉反馈，从而提高了生成CAD模型的质量。 1
  • 通过在大型语言模型中注入视觉反馈实现文本到CAD的生成 . . .
    In this work, we introduce CADFusion, a framework that uses Large Language Models (LLMs) as the backbone and alternates between two training stages: the sequential learning (SL) stage and the visual feedback (VF) stage





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