I2V

Image-to-video (I2V) refers to the set of techniques that generate a video sequence from one or more input images. The goal is to produce temporally coherent frames that preserve content from the input while introducing plausible motion and dynamics. I2V encompasses tasks such as turning a still image into a short animation or generating a sequence conditioned on an initial frame or keyframes.

Common approaches include optical-flow driven methods that interpolate between frames and deep learning models that predict future frames or latent motion representations conditioned on the input. Recent work often uses diffusion models or 3D-aware architectures to improve temporal consistency and realism. Some systems operate in a multi-stage pipeline, first reconstructing structure or texture, then synthesizing subsequent frames to produce a video.

Applications of I2V span content creation, character or scene animation, data augmentation for video models, synthetic data generation for surveillance or robotics, and virtual reality or gaming workflows. I2V can enable rapid prototyping of cinematic shot ideas, enable accessibility by animating still imagery, or provide training data where real video is scarce.

Challenges in I2V include achieving temporal coherence across frames, avoiding visual artifacts and color drift, and maintaining consistent object identity. Computational cost and model training requirements are typically high, and there are data- and copyright-related considerations along with potential misuse for deepfakes and misinformation.

Evaluation of I2V systems uses metrics such as Fréchet Video Distance (FVD) and perceptual similarity measures, complemented by user studies to assess realism and coherence. The field intersects with image-to-image translation, video prediction, and text-to-video research, and it continues to evolve with advances in generative modeling and 3D-aware representations.