顶会论文种子 Semantic Implicit Stylization: Local Texture Editing of Neural Implicit Representations

Title: Semantic Implicit Stylization: Local Texture Editing of Neural Implicit Representations

Abstract:

This paper introduces Semantic Implicit Stylization (SIS), a novel approach for local stylization of 3D objects represented as neural implicit functions. SIS leverages semantic maps to guide the stylization process, enabling fine-grained control over the application of different styles and textures to specific regions of the object. Our method addresses the challenges of stylizing complex and nuanced shapes by combining the flexibility of neural implicit representations with the guidance of semantic information. We demonstrate the effectiveness of SIS on a variety of 3D models, showcasing its ability to generate high-quality and diverse stylized results. Our approach opens up new possibilities for 3D object customization and design, offering a powerful tool for artists and creators.

Keywords:

3D stylization, neural implicit representation, NeRF, semantic map, local editing, texture synthesis, deep learning.

TOC

1. Introduction

   • 1.1 Motivation
   • 1.2 Contributions
   • 1.3 Outline

2. Related Work

   • 2.1 3D Shape Stylization
     • 2.1.1 Text-based Stylization
     • 2.1.2 Image-based Stylization
   • 2.2 Implicit Neural Representations
     • 2.2.1 NeRFs and Variants
     • 2.2.2 Applications of NeRFs
   • 2.3 Semantic Guidance
     • 2.3.1 Semantic Segmentation
     • 2.3.2 Semantic Editing

3. Method (Figure 1)

   • 3.1 Neural Implicit Representation
     • 3.1.1 NeRF Architecture
     • 3.1.2 Training Procedure
   • 3.2 Semantic Mapping (Figure 2)
     • 3.2.1 Semantic Map Acquisition
     • 3.2.2 Semantic Map Processing
   • 3.3 Local Stylization (Figure 3, Algorithm 1, Algorithm 2)
     • 3.3.1 Stylization Network
     • 3.3.2 Integration with NeRF

4. Experiments

   • 4.1 Dataset and Evaluation Metrics
     • 4.1.1 Dataset Description
     • 4.1.2 Evaluation Metrics
   • 4.2 Implementation Details
     • 4.2.1 Network Architecture
     • 4.2.2 Training Parameters
   • 4.3 Results (Figure 4, Figure 5, Table 1, Table 2)
     • 4.3.1 Qualitative Results
     • 4.3.2 Quantitative Results

5. Discussion

   • 5.1 Limitations
   • 5.2 Future Work

6. Conclusion

List of Figures

• Figure 1: Overview of the proposed SIS method.

• Figure 2: Examples of semantic maps for local stylization.

• Figure 3: Visualization of the stylization process.

• Figure 4: Qualitative results of SIS on 3D models.

• Figure 5: Comparison of SIS with baseline methods.

List of Tables

• Table 1: Quantitative results of SIS and baseline methods.

• Table 2: Ablation study of SIS components.

List of Algorithms

• Algorithm 1: Training the stylization network.

• Algorithm 2: Local stylization process.

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