顶会论文种子 Sketch2Shape: AI-powered Local 3D Shape Editing with Sketch-based Guidance

Title: ShapeFlow: AI-Driven Localized 3D Shape Editing Guided by Sketches

Abstract:

3D建模通常要求复杂的步骤和大量时间以实现预期的形状和纹理。为了提供一种高效且精确的局部编辑工具，在此我们引入了Sketch2Shape这一创新方法，在保持3D形状局部编辑效率的同时充分展现了草图直观性。该方法通过深度学习模型实现对用户草图意图的理解，并在特定上下文中生成精确且精细的修改指令。具体而言，模型能够分析输入图像并识别出目标修改类型及其位置信息，并将这些修改无缝整合到原始三维模型中而不影响其整体结构和连贯性。此外Sketch2Shape还支持多种局部编辑操作包括纹理修改形态变形以及新增部分功能等这一系列特性使得该方法能够在不影响原有系统复杂度的前提下显著提升设计效率并降低学习门槛。通过一系列实验以及深入的用户研究我们验证了该方法的有效性和实用性并展示了其在简化设计流程优化创作体验方面的巨大潜力。

Keywords:

3D shape manipulation, sketch-driven interface analysis, human-computer interaction study, deep learning algorithms, computer-generated imagery, generative modeling techniques

Table of Contents

Introduction

 * 1.1 Motivation and Problem Statement 

Address the shortcomings of current 3D modeling tools and highlight the urgent need for more user-friendly and high-performance editing methods.

 * 1.2 Proposed Approach and Contributions 

Present the Sketch2Shape concept and emphasize its main contributions, such as a sketch-based interface, AI-driven local editing, and comprehensive modification options.

 * 1.3 Paper Organization 

Outline the structure of the paper and provide a roadmap for the reader.

Related Work

 * 2.1 3D Shape Editing 
   * 2.1.1 Global Editing Techniques 

Conduct a review of methods that utilize overall alterations in three-dimensional objects, such as style transfer and shape generation.

   * 2.1.2 Local Editing Techniques 

Examine current methods of local 3D shape editing and enumerate their drawbacks.

 * 2.2 Sketch-Based Interfaces 
   * 2.2.1 Sketch Recognition in 2D 

Overview of sketch recognition techniques in 2D image domains.

   * 2.2.2 Sketch-Based 3D Modeling 

Analyze current sketching-based software applications and evaluate their effectiveness in 3D modeling tasks.

 * 2.3 Deep Learning for 3D Shape Manipulation 

Investigate the utilization of deep learning techniques in the fields of 3D shape analysis, generation, and editing.

Sketch2Shape: Methodology

 * 3.1 Overview of the Framework 

Introduce a comprehensive overview of the Sketch2Shape system and its architecture components. Incorporate Figure 1 to demonstrate the process flow.

 * 3.2 Sketch Processing and Feature Extraction 

Describe the processing of user sketches and detail the extraction of relevant features, which are subsequently interpreted. Refer to Figure 2 to understand the process.

 * 3.3 3D Model Representation and Analysis 

本研究采用了一种基于几何建模的方法来描述所使用的3D模型表示方式，并通过分析模型的几何特性来理解其结构和上下文关系。Figure 3展示了一个具体的实例。

 * 3.4 Local Modification Module 
   * 3.4.1 Texture Modification 

阐述基于草图输入的纹理修改方法。
并包含图4a以及算法1。
概述纹理修改的整体流程。

   * 3.4.2 Shape Deformation 

The system illustrates the deformation mechanism of the 3D shape based on the sketch. Refer to Figure 4b and Algorithm 2 for detailed information.

   * 3.4.3 Part Addition 

Explain the procedure of adding new parts or components to a three-dimensional model in accordance with a drawing. Taking into account Figure 4c and Algorithm 3.

Experiments and Evaluation

 * 4.1 Experimental Setup 
   * 4.1.1 Dataset and Evaluation Metrics 

Introduce the dataset utilized for training and evaluation purposes. Define the evaluation metrics, such as accuracy, efficiency, and user satisfaction. Additionally, a summary of the dataset is provided in Table 1.

   * 4.1.2 Implementation Details 

Provide a detailed explanation of how the Sketch2Shape system is implemented, specifically highlighting its underlying deep learning framework and training process.

 * 4.2 Quantitative Results 
   * 4.2.1 Accuracy Evaluation 

This study demonstrates quantitative results on the accuracy of sketch interpretation and modification execution. The findings are detailed in Table 2.

   * 4.2.2 Efficiency Analysis 

The platform's computational performance should be evaluated, and detailed processing times should be reported. The results are to be presented in Table 2.

 * 4.3 Qualitative Results and User Study 
   * 4.3.1 Visual Comparisons 

Compare the edited 3D models using Sketch2Shape alongside other techniques or manual editing. Include Figure 5.

   * 4.3.2 User Feedback 

Summarize the key outcomes of your user study, involving qualitative feedback and usability evaluations. In addition, refer to Table 3 for detailed information.

Discussion and Future Work

 * 5.1 Discussion of Results 

Discuss the key findings from the experiments and their implications.

 * 5.2 Limitations 

Address any limitations of the proposed approach.

 * 5.3 Future Research Directions 

Identify key directions for future research to enhance and expand the Sketch2Shape system.

Conclusion

Summarise the principal contributions of this work and its projected impact on 3D modelling.

Potential impact refers to the long-term consequences or effects resulting from a particular action or condition. In this context, it is used to describe the expected influence of the work on 3D modelling practices.

List of Figures, Tables, and Algorithms

Figures:

• Figure 1: Sketch2Shape 工作流程图
• Figure 2: 基于草图的处理与特征提取
• Figure 3: 三维模型表示与分析过程
• Figure 4a：纹理修改模块示意图
• Figure 4b：形状变形模块说明
• Figure 4c：部分添加功能框图
• Figure 5：定性和对比分析结果展示

Tables:

• 表1：数据概况
• 表2：定量分析（准确率与效率）
• 表3：用户体验反馈

Algorithms:

• Algorithm for Sketch Identification
• Algorithm for Texture Editing
• Algorithm for Shape Manipulation
• Algorithm for Component Augmentation