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arXiv · 2410.00483

MCGM: Mask Conditional Text-to-Image Generative Model

Rami Skaik·Leonardo Rossi·Tomaso Fontanini·Andrea Prati
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Abstract

Recent advancements in generative models have revolutionized the field of artificial intelligence, enabling the creation of highly-realistic and detailed images. In this study, we propose a novel Mask Conditional Text-to-Image Generative Model (MCGM) that leverages the power of conditional diffusion models to generate pictures with specific poses. Our model builds upon the success of the Break-a-scene [1] model in generating new scenes using a single image with multiple subjects and incorporates a mask embedding injection that allows the conditioning of the generation process. By introducing this additional level of control, MCGM offers a flexible and intuitive approach for generating specific poses for one or more subjects learned from a single image, empowering users to influence the output based on their requirements. Through extensive experimentation and evaluation, we demonstrate the effectiveness of our proposed model in generating high-quality images that meet predefined mask conditions and improving the current Break-a-scene generative model.

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Rami Skaik, Leonardo Rossi, Tomaso Fontanini, Andrea Prati. 2024-10-01. MCGM: Mask Conditional Text-to-Image Generative Model. https://arxiv.org/abs/2410.00483

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