Why Your AI Videos Look Fake: 7 Fixes for Common AI Artifacts

7 Common AI Video Problems and How to Fix Them

Introduction: The Uncanny Valley of AI Video

You've spent hours crafting the perfect prompt. You hit generate. The result looks... almost right.

But something's off:

• Faces morph between frames
• Objects drift or disappear
• Lighting flickers unnaturally
• Hands have too many fingers
• Movement feels floaty and ungrounded

Welcome to the uncanny valley of AI video — where outputs are technically impressive but immediately feel "AI-generated."

"AI videos aren't bad because the AI is dumb. They're bad because we don't know how to talk to it."
— Common wisdom from r/VideoEditing

This guide explains why AI videos look fake and provides 7 actionable fixes that work across all major AI video generators — Sora 2, Veo 3, Runway Gen-4, Kling, and others.

Why AI Videos Look Fake: The Technical Reality

Before fixing problems, you need to understand why they exist.

AI predicts frames, it doesn't "understand" physics

Current AI video models work by predicting what the next frame should look like based on previous frames. They don't have:

• A physics engine
• Object permanence
• Understanding of 3D space
• Knowledge of how materials behave

When your prompt doesn't give clear spatial or temporal cues, the AI starts guessing. That's when faces distort, lights flicker, and objects drift.

The "AI aesthetic" is a feature, not a bug

AI videos have developed their own distinctive look — a subtle wrongness that your brain detects even when you can't articulate what's off.

This happens because models are trained on:

• Compressed video data (losing fine detail)
• Mixed quality sources (inconsistent motion)
• Static frames more than motion (better at still moments)

Understanding this helps set realistic expectations and focus your fixes.

The 7 Most Common AI Video Problems (and How to Fix Them)

Problem 1: Face Morphing and Identity Drift

What it looks like: A character's face subtly changes between frames. Features shift, age varies, or the face seems to "melt" during movement.

Why it happens: AI models generate each frame semi-independently. Without strong identity constraints, the model makes different probabilistic choices frame-to-frame.

The fix:

1. Use reference images — Provide a clear character reference image to anchor identity
2. Reduce camera movement — Static or slow-moving cameras maintain consistency better
3. Limit face time — Cut away from faces during dynamic scenes
4. Choose the right model — Kling AI and Runway excel at human face consistency

Pro tip: If a face looks good in frame 1, use that frame as a reference for regeneration.

Problem 2: Object Drift and Disappearing Elements

What it looks like: Objects slowly move across the frame, change size, or vanish entirely. A coffee cup teleports, a car shifts position, a background element disappears.

Why it happens: AI lacks object permanence. Each frame is a new prediction that may or may not include previous objects.

The fix:

1. Anchor key objects in prompts — "A red coffee mug remains on the table throughout"
2. Minimize object count — Fewer objects = less to track = fewer errors
3. Use static compositions — Locked camera positions reduce drift
4. Generate shorter clips — Drift compounds over time; 3-5 seconds is safer than 10+

Problem 3: Lighting Flicker and Exposure Shifts

What it looks like: The scene brightens and darkens randomly. Shadows appear and disappear. Light sources seem to move when they shouldn't.

Why it happens: AI treats lighting as a visual pattern, not a physical phenomenon. It doesn't know that a light source should remain constant.

The fix:

1. Specify lighting in prompts — "Consistent soft daylight from the left, no lighting changes"
2. Avoid mixed lighting — Indoor scenes with single light sources are more stable
3. Use flat lighting styles — Dramatic lighting = more flicker opportunities
4. Post-process stabilization — Color grading tools can normalize exposure

Problem 4: Unnatural Motion and Floaty Movement

What it looks like: Characters glide instead of walk. Objects move without weight. Actions feel dreamlike rather than grounded.

Why it happens: AI learns motion from video data but doesn't understand mass, gravity, or momentum. It mimics the appearance of motion without the physics.

The fix:

1. Describe physics explicitly — "Heavy footsteps with visible ground impact"
2. Reference real motion — "Walking like a tired person carrying groceries"
3. Include environmental interaction — "Feet kicking up dust, hand pressing into cushion"
4. Slow down actions — Slower motion hides floatiness better

Problem 5: The Hand and Finger Problem

What it looks like: Hands have 4, 6, or 7 fingers. Fingers merge, bend wrong, or phase through objects.

Why it happens: Hands are highly variable in training data — different poses, angles, occlusions. The model has seen too many variations to be consistent.

The fix:

1. Hide hands when possible — Frame shots to crop hands or keep them out of focus
2. Use simple hand poses — Fists and open palms are easier than detailed gestures
3. Avoid hand close-ups — Wide shots hide hand issues
4. Regenerate and cherry-pick — Generate multiple versions and select the best hands

2026 update: Veo 3.1 and Sora 2 have significantly improved hand generation, but the problem isn't fully solved.

Problem 6: Temporal Inconsistency (The "Fever Dream" Effect)

What it looks like: The entire scene shifts style, color palette, or composition mid-video. It feels like different videos spliced together.

Why it happens: Longer generations allow more drift from the original prompt. The model's attention to initial instructions weakens over time.

The fix:

1. Generate in short segments — 3-5 second clips, then stitch together
2. Reinforce style in every prompt — Repeat key visual descriptors
3. Use the same seed — Consistent seeds = consistent starting points
4. Apply style frames — Reference images maintain visual consistency

Problem 7: Text and Logo Distortion

What it looks like: Text is unreadable, logos morph, signs show gibberish. Brand elements become abstract patterns.

Why it happens: Text requires precise, pixel-level consistency that conflicts with AI's probabilistic generation. Letters are particularly vulnerable to drift.

The fix:

1. Don't generate text in video — Add text in post-production
2. Blur or hide logos — Avoid including readable text in prompts
3. Use motion graphics overlay — Composite text after generation
4. Accept abstraction — If text must appear, let it be stylized/unreadable

Prompt Engineering Tips to Reduce AI Artifacts

Your prompt is your primary tool for quality control. Here's how to write prompts that minimize fake-looking output.

Be specific about what should NOT change

AI is good at variety. Tell it what to keep consistent:

• "The lighting remains constant throughout"
• "The character's appearance stays identical"
• "Camera position is fixed, no movement"

Describe physics, not just visuals

Instead of: "A ball bounces"

Write: "A rubber ball drops, compresses on impact, bounces back with decreasing height"

Use cinematic language

AI models understand film terminology:

• "Medium shot, locked camera, 50mm lens"
• "Slow push in, steady movement"
• "Natural lighting, no artificial sources"

Include temporal anchors

Guide the AI through time:

• "The scene begins with... then... and finally..."
• "Throughout the entire clip..."
• "At no point does..."

Choosing the Right AI Video Tool for Your Needs

Different tools excel at different things. Match your project to the right generator:

For human faces and realistic characters

Best choice: Kling AI, Runway Gen-4.5

These models have the strongest character consistency and face stability.

For cinematic, artistic content

Best choice: Sora 2, Veo 3.1

Superior visual quality and style control, though may sacrifice some consistency.

For fast iteration and commercial work

Best choice: Genra AI

Optimized for speed and practical output, with built-in quality controls for common artifacts.

For maximum control

Best choice: Runway with Multi-Motion Brush

Granular control over specific regions and movements.

Post-Production Fixes When AI Falls Short

Sometimes the best fix happens after generation:

• Frame interpolation — Smooth out jerky motion
• Color grading — Normalize lighting flicker
• Strategic cuts — Hide problem areas with editing
• Compositing — Replace hands, faces, or text with better versions
• Speed adjustment — Slow motion hides many artifacts

The Future: When Will AI Videos Stop Looking Fake?

AI video quality improves rapidly. In 2025, realism was the obsession. In 2026, creators care more about speed and usability.

Current limitations being solved:

• Physics simulation — Veo 3 already shows improved physical accuracy
• Temporal consistency — Runway's extension tools maintain coherence
• Character identity — Reference-based generation is becoming standard

But some challenges remain:

• Perfect hands are still rare
• Complex multi-character scenes are unstable
• Long-form content requires stitching

The creators who succeed aren't waiting for perfect AI — they're learning to work with current limitations.

Summary: Making AI Videos That Don't Look AI

The key principles:

1. Understand why — AI predicts frames, it doesn't understand physics
2. Write better prompts — Be specific about consistency and physics
3. Choose the right tool — Match generators to your specific needs
4. Work with limitations — Avoid hands, text, and complex lighting
5. Post-process strategically — Fix in editing what AI can't generate
6. Generate short, iterate fast — Quality comes from selection, not single shots
7. Stay current — Tools improve monthly; yesterday's workarounds become unnecessary

The gap between "AI video" and "good video" is closing. With the right techniques, your AI-generated content can look professional today — not just in some future update.

FAQ

Why does my AI video look like a fever dream?

This usually happens with longer generations where the model drifts from initial instructions. Generate in shorter 3-5 second segments and stitch them together for better consistency.

Why are AI video hands always wrong?

Hands are highly variable in training data and require precise consistency that AI struggles with. The best current fix is to avoid hand close-ups or regenerate until you get acceptable results.

Which AI video generator has the fewest artifacts?

For human subjects, Kling AI and Runway Gen-4.5 currently show the best consistency. For overall visual quality, Sora 2 and Veo 3.1 lead. The best choice depends on your specific use case.

Can I fix AI video artifacts in post-production?

Yes. Color grading fixes lighting flicker, frame interpolation smooths motion, and compositing can replace problem areas like hands or faces. Strategic editing (cuts and speed changes) also hides many issues.