Sketch2Prototype is an AI-based framework that transforms a hand-drawn sketch
into a diverse set of 2D images and 3D prototypes through sketch-to-text,
text-to-image, and image-to-3D stages. This framework, shown across various
sketches, rapidly generates text, image, and 3D modalities for enhanced
early-stage design exploration. We show that using text as an intermediate
modality outperforms direct sketch-to-3D baselines for generating diverse and
manufacturable 3D models. We find limitations in current image-to-3D
techniques, while noting the value of the text modality for user-feedback and
iterative design augmentation.

Sketch2Prototype 是一个基于人工智能的框架，通过手绘草图到文本、文本到图像、图像到 3D 的转换过程，快速生成文本、图像和 3D 模型，用于提升早期设计阶段的探索。研究表明，使用文本作为中间模态可以比直接将草图转换为 3D 模型的基准方法产生更加多样且可制造的 3D 模型。同时，发现了现有图像到 3D 技术的局限性，并强调了文本模态在用户反馈和迭代设计中的价值。

快速概念设计探索与原型制作：基于生成人工智能的 Sketch2Prototype

Sketch2Prototype: Rapid Conceptual Design Exploration and Prototyping  with Generative AI

To effectively express and satisfy network application requirements,
intent-based network management has emerged as a promising solution. In
intent-based methods, users and applications express their intent in a
high-level abstract language to the network. Although this abstraction
simplifies network operation, it induces many challenges to efficiently express
applications' intents and map them to different network capabilities.
Therefore, in this work, we propose an AI-based framework for intent profiling
and translation. We consider a scenario where applications interacting with the
network express their needs for network services in their domain language. The
machine-to-machine communication (i.e., between applications and the network)
is complex since it requires networks to learn how to understand the domain
languages of each application, which is neither practical nor scalable.
Instead, a framework based on emergent communication is proposed for intent
profiling, in which applications express their abstract quality-of-experience
(QoE) intents to the network through emergent communication messages.
Subsequently, the network learns how to interpret these communication messages
and map them to network capabilities (i.e., slices) to guarantee the requested
Quality-of-Service (QoS). Simulation results show that the proposed method
outperforms self-learning slicing and other baselines, and achieves a
performance close to the perfect knowledge baseline.

为了有效表达和满足网络应用的需求，我们提出了一种基于人工智能的框架，用于意图识别和翻译。该框架基于新兴通信实现意图识别，应用通过通信消息表达他们的抽象化用户体验意图，网络学习如何解释这些通信消息并映射到网络能力，从而保证所需的服务质量。模拟结果显示，该方法胜过自学习分片方法和其他基准方法，接近完美知识基准。