MCGM: Mask Conditional Text-to-Image Generative Model
<|reference_start|>MCGM: Mask Conditional Text-to-Image Generative Model: 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.<|reference_end|>
@article{skaik2024mcgm:,
    title={MCGM: Mask Conditional Text-to-Image Generative Model},
    author={Rami Skaik, Leonardo Rossi, Tomaso Fontanini, and Andrea Prati},
    journal={arXiv preprint arXiv:2410.00483},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00483},
    primaryClass={cs.CV cs.AI}
}