Frame Interpolation

Frame interpolation is an AI video technique that generates entirely new, physically plausible in-between frames sitting between two existing consecutive frames of a source video, used to smooth perceived motion or convert footage from a lower to a higher frame rate (e.g., 24fps to 60fps) without simply duplicating or cross-fading frames, which is what older, non-AI interpolation methods did and which produced visible ghosting/blurring artifacts on fast motion. Neural frame-interpolation models (RIFE, DAIN, and the interpolation modules built directly into tools like Topaz Video AI and most modern AI video generators) estimate optical flow — a dense, pixel-by-pixel motion vector field describing precisely how content moved and where it will land between the two source frames — and use that motion estimate to synthesize a genuinely new intermediate frame with every object positioned correctly along its actual motion path at that fractional point in time, rather than simply cross-fading or blending raw pixel values between the two frames, which produces far smoother, more natural-looking motion, especially for fast-moving objects or complex occlusion (an object briefly passing behind another) — cases where naive frame-blending produces obvious, jarring double-exposure-style ghosting artifacts that immediately look wrong to a viewer. Why it matters for SaaS builders: frame interpolation is most directly relevant as an internal pipeline component of AI video-synthesis tools — many text-to-video and video-synthesis models natively generate at a lower frame rate (e.g., 8-16fps) for cost/speed reasons, then apply frame interpolation as a cheaper post-processing pass to reach a smooth, standard delivery frame rate (24-60fps) rather than generating every frame directly with the much more expensive core diffusion model. It's also relevant to video-restoration/remastering SaaS products upconverting old low-frame-rate archival footage for modern displays, and to slow-motion effect tools that interpolate extra frames to stretch footage smoothly rather than simply slowing down existing frames (which looks choppy). A concrete worked example — an AI video-generation platform's cost-optimized rendering pipeline: (1) to keep GPU costs manageable, the core video-diffusion model generates a clip at 12fps; (2) rather than paying the much higher compute cost to generate natively at 24fps, the platform runs the raw 12fps output through a frame-interpolation model that generates a new synthesized frame between every pair of existing frames; (3) the result is a smooth 24fps final video at a fraction of the generation cost of native 24fps diffusion; (4) the interpolation step adds roughly 5-10 seconds of processing versus multiplying the core generation cost by 2x.