تقرير
DiffusionAct: Controllable Diffusion Autoencoder for One-shot Face Reenactment
| العنوان: | DiffusionAct: Controllable Diffusion Autoencoder for One-shot Face Reenactment |
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| المؤلفون: | Bounareli, Stella, Tzelepis, Christos, Argyriou, Vasileios, Patras, Ioannis, Tzimiropoulos, Georgios |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence |
| الوصف: | Video-driven neural face reenactment aims to synthesize realistic facial images that successfully preserve the identity and appearance of a source face, while transferring the target head pose and facial expressions. Existing GAN-based methods suffer from either distortions and visual artifacts or poor reconstruction quality, i.e., the background and several important appearance details, such as hair style/color, glasses and accessories, are not faithfully reconstructed. Recent advances in Diffusion Probabilistic Models (DPMs) enable the generation of high-quality realistic images. To this end, in this paper we present DiffusionAct, a novel method that leverages the photo-realistic image generation of diffusion models to perform neural face reenactment. Specifically, we propose to control the semantic space of a Diffusion Autoencoder (DiffAE), in order to edit the facial pose of the input images, defined as the head pose orientation and the facial expressions. Our method allows one-shot, self, and cross-subject reenactment, without requiring subject-specific fine-tuning. We compare against state-of-the-art GAN-, StyleGAN2-, and diffusion-based methods, showing better or on-par reenactment performance. Comment: Project page: https://stelabou.github.io/diffusionact/ |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.17217 |
| رقم الأكسشن: | edsarx.2403.17217 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |