Image to video Generation: Using Deep Learning And Diffusion Model

Anonymous

6pages , 2 figure

Abstract

Image-to-video generation is an emerging area of artificial intelligence that focuses on converting static images into realistic video sequences using deep learning techniques. Recent advances in generative artificial intelligence, especially diffusion models, Generative Adversarial Networks (GANs), and transformer architectures, have significantly improved the quality, temporal consistency, and realism of generated videos. This paper presents a comprehensive study of image-to-video generation systems, including their methodologies, architectures, datasets, evaluation metrics, applications, challenges, and future directions. The study also reviews popular modern frameworks such as Stable Video Diffusion, AnimateDiff, Runway Gen-2, and Sora. The increasing demand for automated video synthesis in entertainment, gaming, education, healthcare, and virtual reality has accelerated research in this domain. Despite rapid progress, challenges such as motion consistency, computational complexity, long-duration generation, and ethic-al concerns related to deepfakes remain significant research problems. The paper concludes by discussing future opportunities in real-time video generation, controllable motion synthesis, and multimodal generative systems.

Submitted: May 29, 2026Subjects: Artificial Intelligence; Research Paper

Description / Details

The ability to synthesize realistic video content from a single input image has rapidly emerged as a transformative frontier in generative modeling. Unlike traditional video synthesis approaches that rely on densely annotated video datasets or sequential frame prediction, image-to-video generation attempts to infer both the temporal evolution and motion dynamics that the single image alone does not explicitly reveal

Because natural scenes are inherently ambiguous with respect to future motion, the task must reconcile two conflicting objectives:privacy concerns because data is processed on third-party servers.

The task must reconcile two conflicting objectives: preserving the static visual attributes of the input image while generating plausible temporal transformations that remain coherent, interpretable, and visually convincing. Achieving this balance remains challenging due to the high dimensionality of temporal data, the uncertainty of motion trajectories, and the need for temporal-spatial consistency across generated frames.