AI Keyframe Interpolation

AI keyframe interpolation synthesizes new video frames between existing captured frames, increasing the frame rate of video content by generating intermediate frames that represent the motion state between adjacent captured frames. Traditional frame interpolation used simple optical flow estimation — calculating how pixels moved between frames and blending intermediate positions proportionally. AI-based interpolation uses deep neural networks to understand object boundaries, motion vectors, and scene structure, generating synthesized intermediate frames that correctly handle occluding objects (where foreground elements pass in front of background elements), accurately represent motion for different types of movement, and maintain visual quality at the synthesized frames. The leading AI frame interpolation model, RIFE (Real-Time Intermediate Flow Estimation), runs at near-real-time speeds and is integrated into several video editing tools.

Frame interpolation enables slow-motion effects from footage not captured at high speed. Standard video capture at 24-30fps contains insufficient frames to display as slow motion without creating choppy playback; high-speed cameras (120fps, 240fps, or higher) capture enough frames for smooth slow motion but are expensive and logistically complex. AI frame interpolation can increase a 30fps clip to 120fps or higher by synthesizing 3-7 intermediate frames between each pair of captured frames, producing usable slow-motion effects from standard camera footage. The quality of the synthesized frames depends on the motion complexity — smooth, predictable motion (flowing water, simple lateral movement) interpolates more accurately than complex, fast motion (sport footage, crowd scenes, fast hand gestures) where the model has more uncertainty about what should appear between frames.

For B2B video production, AI frame interpolation has two primary applications. Slow-motion enhancement: footage shot at standard frame rates can be converted to slow motion for dramatic product reveals, detailed process demonstrations, or emotional storytelling moments where the natural pacing would benefit from temporal stretching. Frame rate conversion: content originally captured at different frame rates for different delivery requirements (24fps cinematic, 30fps broadcast, 60fps gaming) can be converted between rates without the motion artifacts of traditional interpolation — relevant for repurposing content across platforms with different frame rate expectations. Both applications are accessible through production tools like Davinci Resolve's Optical Flow Enhanced, Topaz Video AI, and Adobe Premiere's Time Remapping with AI interpolation, requiring no specialized capture hardware.