Best AI Tools for Inbetweening: Top Frame Interpolators

AI inbetweening is changing how animators and video editors fill motion gaps. “Best AI Tools for Inbetweening” isn’t just a list—it’s a practical map for picking software that actually speeds production without wrecking style. Whether you want smoother 2D animation, higher FPS for footage, or automated frame creation for rotoscoping, AI frame interpolation tools can save hours. In my experience, the trick is matching the tool to the shot: some excel at realistic video motion, others at stylized animation. Below I break down the best options, real-world tips, and clear trade-offs so you can test fast and get usable frames.

Why AI inbetweening matters

Traditional inbetweening (or tweening) is laborious. AI promises faster results by generating intermediate frames from keyframes or consecutive frames.

Benefits:

• Speeds up production cycles
• Reduces repetitive manual drawing
• Improves frame-rate conversion for video

But it’s not a magic wand. AI can introduce artifacts, so expect iteration—especially on stylized artwork.

How to choose an AI inbetweening tool

Ask these questions:

• Is the target footage stylized or photoreal?
• Do I need real-time previews or highest-quality offline renders?
• What’s my budget (free, open-source, or commercial)?

Top AI tools for inbetweening (hands-on breakdown)

Below are the tools I test most often. Each entry lists strengths, weaknesses, and best use cases.

1. RIFE (Real-Time Intermediate Flow Estimation)

RIFE is a fast, neural flow-based frame interpolator that gives very smooth motion in many cases. The algorithm is available via GitHub builds and integrated into GUIs like Flowframes.

Best for: quick interpolation of video and experimental animation tests.

Pros: very fast, open-source, excellent default motion.

Cons: can blur fine line art and struggle with extreme stylization.

2. DAIN (Depth-Aware Video Frame Interpolation)

DAIN uses depth estimation to handle occlusions better, which helps in complex 3D-like motion. Quality is high but it’s heavier to run.

Best for: footage with depth/overlap where object separation matters.

Pros: better occlusion handling, good for layered scenes.

Cons: slower, requires more compute.

3. Topaz Video AI (commercial)

Topaz combines proprietary models for frame interpolation and enhancement. It’s polished, user-friendly, and aimed at pros who need consistent results without fiddly setup. See official product details on the Topaz Video AI site.

Best for: creators who want plug-and-play, high-quality output.

Pros: easy UI, consistent results, good support.

Cons: paid license; some models soften art styles.

4. Runway (cloud-based)

Runway offers cloud models for frame interpolation and creative motion tools. It’s great for quick experimentation and team workflows—no heavy local GPU needed. Learn more on the Runway official site.

Best for: fast cloud testing and collaboration.

Pros: no install, easy sharing, modern UX.

Cons: subscription costs and upload privacy considerations.

5. Flowframes (GUI wrappers)

Flowframes provides a user-friendly front-end for RIFE, DAIN and other algorithms. If you want a Windows GUI to test different models quickly, this is handy.

Best for: animators who want local, fast tests across multiple engines.

6. EbSynth (for stylized frames)

EbSynth isn’t a frame interpolator per se, but it can transfer painted keyframes to whole clips and is fantastic for preserving style across frames. Use it alongside frame interpolators to keep artistic consistency.

Best for: maintaining painterly or hand-drawn styles.

7. DAIN, RIFE forks & research implementations

There are evolving community forks and research papers (RIFE, DAIN, others) that push quality further. If you like tweaking, the GitHub research builds can yield great customized results.

Comparison table: features at a glance

Practical workflow tips (what I’ve learned)

• Start with low-res tests. Cheap and fast. If it looks bad, you saved time.
• Combine tools: use RIFE for motion then EbSynth to reapply style.
• Mask problem areas (e.g., faces, hands) and perform manual touch-ups—AI struggles with tiny, critical details.
• Keep source frames clean: high-contrast line art and consistent background help the model.
• Use optical-flow visualizers and compare outputs frame-by-frame before committing.

When AI inbetweening fails (and how to recover)

Artifacts, warps, and blurring happen. If the AI breaks a shot:

• Switch to a depth-aware model (like DAIN) for occlusion issues.
• Reduce interpolation factor—interpolate in smaller steps.
• Blend AI frames with manual inbetweens using cross-dissolve and painterly fixes.

Resources & further reading

For background on traditional tweening see tweening on Wikipedia. For commercial options and docs, check the Topaz Video AI official page and cloud features on Runway. If you like research, search for the RIFE and DAIN papers for technical details.

Final thoughts

AI inbetweening is no longer niche. It’s a practical helper that speeds up animation and video post-production—but it’s not a one-click replacement for craft. Try a workflow that mixes a fast interpolator (RIFE/Flowframes) and a style-preserving tool (EbSynth) for best results. Test early, iterate, and treat AI as an assistant that gets you most of the way there.