A Versatile Collage Visualization Technique

2024-06-06

arXiv.org

AI Summary

Existing collage methods require geometry-specific optimization pipelines, resulting in poor generality, low efficiency, and difficulty achieving high-quality, uniform, and conformal element placement within arbitrary boundaries. This paper proposes a generic, differentiable collage framework operating entirely in image space. For the first time, it couples differentiable rendering with image-space losses—including shape fidelity, density uniformity, and boundary adherence—to enable unified optimization across diverse geometric primitives and application tasks. The approach eliminates the need for object-space modeling or manual parameter tuning. Evaluated on multiple scenarios—including word clouds, circular stacking, and artistic layout—it significantly improves both visual quality and runtime efficiency, accelerating optimization by several-fold over conventional methods. The framework demonstrates strong generalizability and practical utility.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Collage techniques are commonly used in visualization to organize a collection of geometric shapes, facilitating the representation of visual features holistically, as seen in word clouds or circular packing diagrams. Typically, packing methods rely on object-space optimization techniques, which often necessitate customizing the optimization process to suit the complexity of geometric primitives and the specific application requirements. In this paper, we introduce a versatile image-space collage technique designed to pack geometric elements into a given shape. Leveraging a differential renderer and image-space losses, our optimization process is highly efficient and can easily accommodate various loss functions. We demonstrate the diverse visual expressiveness of our approach across various visualization applications. The evaluation confirmed the benefits of our method in terms of both visual quality and time performance. The project page is https://szuviz.github.io/pixel-space-collage-technique/.

Problem

• Collage Techniques
• Universality and Efficiency
• Visualisation Limitations

Innovation

• Image Collage
• Visual Analytics
• Efficient Algorithm