تقرير
UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer
العنوان: | UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer |
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المؤلفون: | Cheong, Soon Yau, Mustafa, Armin, Gilbert, Andrew |
المصدر: | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops 2023 |
سنة النشر: | 2023 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence |
الوصف: | Text-to-image models (T2I) such as StableDiffusion have been used to generate high quality images of people. However, due to the random nature of the generation process, the person has a different appearance e.g. pose, face, and clothing, despite using the same text prompt. The appearance inconsistency makes T2I unsuitable for pose transfer. We address this by proposing a multimodal diffusion model that accepts text, pose, and visual prompting. Our model is the first unified method to perform all person image tasks - generation, pose transfer, and mask-less edit. We also pioneer using small dimensional 3D body model parameters directly to demonstrate new capability - simultaneous pose and camera view interpolation while maintaining the person's appearance. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2304.08870 |
رقم الأكسشن: | edsarx.2304.08870 |
قاعدة البيانات: | arXiv |
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