FILM

FILM (Frame Interpolation for Large Scene Motion) is a deep learning model from Google Research designed to generate intermediate video frames between existing frames, even in the presence of large camera or object motion. Hosted on Replicate, this implementation lets users run FILM in the cloud without setting up complex ML environments. Users can upload two or more input frames or a video clip and specify how many intermediate frames they want to generate, effectively increasing frame rate or creating smooth slow-motion sequences.

The model is particularly effective for challenging scenarios such as fast-moving subjects, significant viewpoint changes, or complex scene dynamics where traditional optical-flow-based methods struggle. Typical use cases include smoothing low frame rate footage, generating slow motion from standard video, improving temporal consistency in animations, and creating in-between frames for visual effects workflows. Developers can integrate FILM into their pipelines using Replicate’s API, enabling automated video enhancement, content creation tools, and production workflows that require high-quality motion interpolation without extensive manual editing or proprietary software.