Neural Collage Transfer: Artistic Reconstruction via Material Manipulation
Ganghun Lee, Minji Kim, Yunsu Lee, Minsu Lee, Byoung-Tak Zhang

TL;DR
This paper introduces a reinforcement learning-based method for creating artistic collages by intelligently selecting and arranging diverse materials to reconstruct target images, emphasizing aesthetic quality and style fidelity.
Contribution
It presents a novel reinforcement learning framework with a model-based soft actor-critic for collage creation, addressing the lack of data and complexity in the process.
Findings
The method effectively reconstructs target images as collages with high content and style scores.
The approach outperforms pixel-wise generation methods in aesthetic evaluation.
Active material selection improves collage quality and visual appeal.
Abstract
Collage is a creative art form that uses diverse material scraps as a base unit to compose a single image. Although pixel-wise generation techniques can reproduce a target image in collage style, it is not a suitable method due to the solid stroke-by-stroke nature of the collage form. While some previous works for stroke-based rendering produced decent sketches and paintings, collages have received much less attention in research despite their popularity as a style. In this paper, we propose a method for learning to make collages via reinforcement learning without the need for demonstrations or collage artwork data. We design the collage Markov Decision Process (MDP), which allows the agent to handle various materials and propose a model-based soft actor-critic to mitigate the agent's training burden derived from the sophisticated dynamics of collage. Moreover, we devise additional…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis · Generative Adversarial Networks and Image Synthesis
MethodsBalanced Selection
