Im2Vide0: A Zero-Shot approach using diffusion models for natural language conditioned Image-to-Video

Recent breakthroughs in the study of Denoising Diffusion Models have constituted a driving factor in advancing the field of Computer Vision through the aid of generative Machine Learning. Challenging problems, such as image or video synthesis, become intertwined with Natural Language Processing techniques to push the boundaries of computational capabilities and foster human ingenuity. We present an approach to solving text-guided video completion, a difficult task of producing the temporal future progression of an image, conditioned by textual description. Our proposed framework applies the latest academic leaps in Text-to-Image and Text-to-Video generation to perform Image Animation guided by text, an insufficiently explored topic due to its strenuous nature. We present how adapting generalized pre-trained Text-to-Image models can achieve good performance solving the described problem in a Zero-Shot context. Our experimental evaluation shows that the proposed solution is robust, capable of animating any source image guided by a language prompt, by inheriting the generality of the backbone pre-trained Text-to-Image model.