Frame Interpolation

1. pip install rife-ncnn-vulkan (RIFE — Real-Time Intermediate Flow Estimation, the standard open-weight frame interpolation).
2. Also: git clone https://github.com/hzwer/Practical-RIFE for the full Python implementation.
3. Command-line interpolation: rife-ncnn-vulkan -i input_frames/ -o output_frames/ -m rife-v4.6 -n 2 (doubles frame rate by inserting 1 frame between each pair).
4. For a 30 fps video → 60 fps: RIFE processes ~30-60 fps on RTX 3060 (real-time for 1080p).
5. For more extreme interpolation (8× slow-mo): -n 8 inserts 7 frames between each pair — 30 fps → 240 fps. Takes 3-5× real-time.
6. Alternative for higher quality: FILM (Frame Interpolation for Large Motion, Google Research) via pip install film-interpolation. Better at large motion but 3-5× slower than RIFE.
7. Use cases: slow-motion, frame rate upscaling for video editing, smooth animation from keyframes.

Frame interpolation is extremely GPU-efficient. RIFE runs in real-time (30-60 fps for 1080p) on a used GTX 1060 6 GB (~$60). For 4× slow-mo (30→120 fps): ~15-20 fps processing on GTX 1060 — a 1-minute video takes 3-4 minutes. Pair with any CPU + 16 GB RAM + 512 GB NVMe. Total: ~$270-330. For CPU-only: RIFE-ncnn-Vulkan runs at 5-10 fps for 1080p on modern CPUs — slow but functional. Frame interpolation is one of the lightest AI video tasks. Even integrated graphics (Intel Iris Xe) handles real-time 720p.

Used RTX 3060 12 GB (~$200-250, see /hardware/rtx-3060-12gb) is overkill for interpolation. RIFE processes 4K video at 30-60 fps — real-time for most workflows. FILM (higher quality) processes 1080p at 10-15 fps. For a professional video editor: RTX 3060 is the end-game GPU for interpolation. You'd need exotic workloads (8K at 60 fps, batch processing 100s of hours of footage) to justify more GPU. Total build: ~$700-900. Interpolation is the least GPU-intensive video AI task — spend your budget on storage for high-bitrate video.

The mistake: Running RIFE at 8× interpolation on a video with fast motion (sports, action scenes) and getting ghosting artifacts and warped frames. Why it fails: RIFE estimates optical flow between frames. When motion is large (a soccer ball moving 100px between frames at 30 fps), the flow estimation breaks down — the algorithm can't find where each pixel went. It generates a blurry average of the two frames instead of a true intermediate. The fix: Use FILM for large-motion interpolation — it uses a multi-scale approach that handles large displacements better. Or: shoot at a higher base frame rate (60 fps → interpolate to 240 fps instead of 30→240). The smaller the pixel displacement between frames, the better interpolation works. Slow-mo works best when the base footage has enough temporal information (60+ fps). "Enhance" doesn't create information from nothing.