AI Tools for Video Frame Interpolation

Last updated: March 15, 2026

layout: default title: “AI Tools for Video Frame Interpolation” description: “A practical guide to AI-powered video frame interpolation tools for developers and power users, with implementation examples and tool comparisons” date: 2026-03-15 last_modified_at: 2026-03-15 author: theluckystrike permalink: /ai-tools-for-video-frame-interpolation/ voice-checked: true score: 9 reviewed: true intent-checked: true tags: [ai-tools-compared, artificial-intelligence] categories: [guides] —

Video frame interpolation transforms footage by generating intermediate frames between existing ones, effectively increasing the frame rate or creating smooth slow-motion effects. AI-powered tools have dramatically improved the quality of interpolated frames, moving beyond traditional optical flow methods to neural network approaches that produce more natural results.

Key Takeaways

Tile large frames. For 4K and above: most models support tile-based inference that splits the frame into overlapping chunks, processes each chunk, and blends the results.
What resolution inputs do: these models support? Most models accept any resolution but are trained on standard resolutions (720p, 1080p).
At 4K resolution: expect to need at least 8GB of GPU memory for most models.
Use lossless intermediate frames.: Saving intermediate frames as PNG or using a lossless codec (like FFV1) prevents compression artifacts from accumulating across the interpolation pipeline.
Extract a 10-second representative: clip that includes fast motion, panning, and static shots, and use it for all your configuration testing before committing to a full run.
If you already have: 120fps footage you want to play at 24fps, use a re-timing approach in your video editor rather than interpolation.

Understanding Frame Interpolation Approaches

Traditional frame interpolation relies on optical flow algorithms that track pixel movement between frames and generate intermediate positions. These methods struggle with occlusions, motion blur, and complex scene changes. AI-based approaches train neural networks to predict intermediate frames based on learned patterns from large video datasets, producing superior results for challenging footage.

Modern frame interpolation models fall into several categories: flow-based methods that estimate motion vectors, phase-based approaches that work in the frequency domain, and kernel-based techniques that generate pixels through learned convolution kernels. Each has tradeoffs between inference speed, quality, and computational requirements.

Practical Tools and Implementation

RIFE (Real-Time Intermediate Flow Estimation)

RIFE is an open-source frame interpolation model that balances quality and speed effectively. It uses a hierarchical feature flow algorithm that processes frames at multiple scales, achieving strong results for general video content.

import torch
from riffusion.riffusion import RiffusionPipeline

# Load the model
pipeline = RiffusionPipeline.from_pretrained(
    "riffusion/riffusion-model-v1"
).to("cuda")

# Interpolate between two frames
interpolated_frame = pipeline.interpolate(
    frame_a=first_frame,
    frame_b=second_frame,
    num_intermediate_frames=1
)

RIFE performs well for standard video content but may struggle with fine details in high-motion sequences.

Real-ESRGAN for Enhancement

While primarily designed for image upscaling, Real-ESRGAN can enhance interpolated frames by reducing artifacts. Combining frame interpolation with upscaling creates higher-quality slow-motion footage from standard definition sources.

from realesrgan_ncnn_vulkan import RealESRGAN

# Initialize with GPU acceleration
upscaler = RealESRGAN(gpu_id=0, scale=2)

def interpolate_and_enhance(video_path, output_path):
    frames = extract_frames(video_path)
    interpolated = []

    for i in range(len(frames) - 1):
        # Generate intermediate frame
        mid_frame = interpolate(frames[i], frames[i+1])
        # Enhance quality
        enhanced = upscaler.process(mid_frame)
        interpolated.extend([frames[i], enhanced])

    interpolated.append(frames[-1])
    save_video(interpolated, output_path)

FFmpeg with AI Filters

FFmpeg now includes filter support for AI-powered frame interpolation through the minterpolate filter and integration with external models.

# Basic frame interpolation with FFmpeg
ffmpeg -i input.mp4 -vf "minterpolate=fps=60:mi_mode=mci:mc_mode=aobmc:vsbmc=1" output.mp4

# Using the enhanced optical flow mode
ffmpeg -i input.mp4 -filter_complex "[0:v]minterpolate=fps=120:mi_mode=opencv:search_param=梁" output.mp4

The minterpolate filter uses motion-compensated interpolation and can be combined with other FFmpeg filters for video processing pipelines.

Open-Source Implementation Options

DAIN (Depth-Aware Video Frame Interpolation)

DAIN inserts explicitly detected depth to handle occlusions better than flow-only methods. The model architecture separates flow estimation, depth prediction, and frame synthesis into distinct components.

import dain_pytorch as dain

model = dain.DAIN(
    channels=3,
    depth_estimator='monodepth',
    flow_estimator='pytorchl flownet2'
).cuda()

def interpolate_frames(frame1, frame2, depth1, depth2):
    with torch.no_grad():
        flow = model.estimate_flow(frame1, frame2)
        depth = model.estimate_depth(frame1)
        intermediate = model.synthesize(frame1, frame2, flow, depth)
    return intermediate

Anime Interpolation Specialized Tools

For anime and animated content, specialized tools like RIFE-NCNN handle the distinct motion patterns better than general-purpose models. These tools optimize for the clean lines and predictable motion typical of animation.

from rifemulator import RIFENCNN

# Optimized for anime content
anime_interpolator = RIFENCNN(
    model_path="./models/anime-rife-v4",
    gpu_enabled=True,
    tile_size=512
)

result = anime_interpolator.interpolate(
    "input_frames/",
    "output_frames/",
    multiplier=2  # Double the frame rate
)

Building a Processing Pipeline

For production use, consider organizing your frame interpolation workflow into discrete steps:

class FrameInterpolationPipeline:
    def __init__(self, config):
        self.detector = SceneDetector(threshold=config.scene_threshold)
        self.interpolator = self._load_interpolator(config.model)
        self.enhancer = EnhancementProcessor() if config.enhance else None

    def process(self, input_video, output_video, target_fps):
        scenes = self.detector.detect_scenes(input_video)

        for scene in scenes:
            frames = self._extract_scene_frames(scene)
            interpolated = self._interpolate_scene(
                frames,
                target_fps
            )

            if self.enhancer:
                interpolated = self.enhancer.process_batch(interpolated)

            self._write_frames(interpolated, output_video)

    def _interpolate_scene(self, frames, target_fps):
        results = []
        for i in range(len(frames) - 1):
            gap = target_fps // self.source_fps
            intermediate = self.interpolator.generate(
                frames[i],
                frames[i + 1],
                num_frames=gap - 1
            )
            results.extend([frames[i]] + intermediate)
        results.append(frames[-1])
        return results

Scene detection prevents artifacts at cut points since interpolation across scene boundaries produces unreliable results. Processing each scene separately maintains quality throughout the output.

Performance Considerations

Frame interpolation is computationally intensive. GPU acceleration significantly reduces processing time, with most models optimized for CUDA or Vulkan backends. For batch processing, consider processing videos in parallel across multiple GPUs or distributing frames across a compute cluster.

Memory requirements scale with resolution and model complexity. At 4K resolution, expect to need at least 8GB of GPU memory for most models. Tile-based processing allows handling higher resolutions but increases processing time.

Tool Comparison Table

Choosing the right interpolation tool depends on your content type, hardware, and quality requirements. Here is a direct comparison of the main options:

Tool	Best For	Speed	GPU Required	Open Source	Notable Strength
RIFE	General video	Fast	Yes (CUDA)	Yes	Real-time capable on modern GPUs
DAIN	Footage with occlusions	Moderate	Yes	Yes	Depth-aware synthesis
RIFE-NCNN	Anime / animation	Fast	Vulkan	Yes	Works on AMD and Intel GPUs
Real-ESRGAN	Post-enhancement	Moderate	Yes	Yes	Removes interpolation artifacts
FFmpeg minterpolate	Quick batch jobs	Fast	No	Yes	No model download needed
Topaz Video AI	Professional output	Slow	Yes	No	Best quality, paid license

For developers building pipelines on a budget, RIFE with RIFE-NCNN as a fallback for non-NVIDIA hardware covers most use cases. Topaz Video AI is the right choice when quality is the only metric that matters and processing time is not a constraint.

Step-by-Step Workflow: Doubling Frame Rate on Existing Footage

This workflow converts a 24fps video to 48fps using RIFE on a CUDA-capable machine.

Step 1: Extract frames from the source video.

mkdir -p frames/input
ffmpeg -i source_video.mp4 -qscale:v 1 frames/input/%06d.png

Step 2: Run RIFE interpolation on consecutive frame pairs.

import os
from pathlib import Path

input_dir = Path("frames/input")
output_dir = Path("frames/output")
output_dir.mkdir(parents=True, exist_ok=True)

frame_files = sorted(input_dir.glob("*.png"))

for i in range(len(frame_files) - 1):
    frame_a = load_frame(frame_files[i])
    frame_b = load_frame(frame_files[i + 1])

    # Write original frame
    save_frame(frame_a, output_dir / f"{i*2:06d}.png")

    # Write interpolated frame
    mid = rife_model.interpolate(frame_a, frame_b)
    save_frame(mid, output_dir / f"{i*2+1:06d}.png")

# Write final frame
save_frame(load_frame(frame_files[-1]), output_dir / f"{(len(frame_files)-1)*2:06d}.png")

Step 3: Reassemble into a video.

ffmpeg -framerate 48 -i frames/output/%06d.png \
  -c:v libx264 -crf 18 -pix_fmt yuv420p \
  output_48fps.mp4

Step 4: Verify timing and quality. Play the output at 48fps and check for ghosting around fast-moving objects. If artifacts appear at specific timestamps, those correspond to scene changes where the interpolator crossed a hard cut. Run scene detection and re-process those segments without interpolation.

Pro Tips for Better Results

Always detect scene cuts before interpolating. Generating a frame between the last frame of one shot and the first frame of the next produces a bizarre blend. Tools like PySceneDetect identify cut points so you can skip interpolation at those boundaries.

Use lossless intermediate frames. Saving intermediate frames as PNG or using a lossless codec (like FFV1) prevents compression artifacts from accumulating across the interpolation pipeline. Only apply lossy compression at the final output stage.

Test with a short clip first. Interpolating a full video can take hours. Extract a 10-second representative clip that includes fast motion, panning, and static shots, and use it for all your configuration testing before committing to a full run.

Match your target frame rate to your display. 60fps looks different than 59.94fps on many displays. If you are targeting broadcast or streaming platforms, confirm the exact frame rate specification before running the pipeline.

Tile large frames. For 4K and above, most models support tile-based inference that splits the frame into overlapping chunks, processes each chunk, and blends the results. This allows processing resolutions that would otherwise exhaust GPU memory, at the cost of longer processing time.

Selecting the Right Tool

Choose based on your specific requirements:

For quality-first work, DAIN or RIFE-NCNN produce the best interpolated frames. When speed matters, FFmpeg’s minterpolate handles real-time or near-real-time needs. Anime content benefits from specialized models tuned for clean lines and predictable motion. Production pipelines should be modular, with discrete scene detection and enhancement stages.

Newer models handle edge cases better and run faster on consumer hardware than they did a year ago. Test multiple approaches against your actual content types — the right balance of quality and speed is content-specific.

Frequently Asked Questions

Does frame interpolation work on slow-motion footage already shot at high speed? Frame interpolation is designed to synthesize frames that do not exist, not to re-time footage. If you already have 120fps footage you want to play at 24fps, use a re-timing approach in your video editor rather than interpolation.

Can interpolation fix interlaced video? Deinterlace before interpolating. Running interpolation on interlaced frames produces severe artifacts because the algorithm cannot reconcile the alternating field structure.

What resolution inputs do these models support? Most models accept any resolution but are trained on standard resolutions (720p, 1080p). Very high resolution inputs may require tiling, and very low resolution inputs may benefit from upscaling before interpolation rather than after.

How do I handle audio when changing frame rate? The audio stream is independent of the frame rate. Copy the audio stream unchanged (-c:a copy in FFmpeg) and let the container handle synchronization. Only the video PTS values change when you alter the frame rate.

Is interpolation detectable? Trained reviewers can often identify interpolated footage from characteristic motion artifacts, particularly around thin objects and fast lateral movement. For archival or forensic use cases, always preserve the original alongside the interpolated version.

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