8 Things AI Video Generators Still Can't Do Well in 2025: The Current Limitations

What Are AI Video Generators and Why Understanding Their Limitations Matters

AI video generators have revolutionized content creation in 2025, with tools like OpenAI's Sora, Runway Gen-3, and Pika Labs producing impressive results. However, despite rapid advancements, these systems still struggle with fundamental challenges that limit their practical applications. Understanding these limitations is crucial for content creators, marketers, and businesses planning to integrate AI video into their workflows.

According to Meta's AI research team, current text-to-video models face significant challenges in temporal consistency, physics understanding, and fine-grained control. These aren't just minor inconveniences—they represent fundamental gaps in how AI understands and generates video content.

"While AI video generation has made remarkable progress, we're still years away from systems that can reliably handle complex narratives, precise object interactions, and consistent character representations across extended sequences."

Dr. Alexei Efros, Professor of Computer Science, UC Berkeley

This comprehensive guide explores eight critical areas where AI video generators consistently fall short, helping you set realistic expectations and plan effective workarounds for your video projects.

1. Maintaining Consistent Characters Across Scenes

One of the most glaring limitations of current AI video generators is their inability to maintain character consistency across multiple shots or scenes. While tools can generate impressive individual clips, characters often change appearance, clothing, or even fundamental features between generations.

Why This Happens

AI video models generate content frame-by-frame or in short sequences, lacking a persistent "memory" of character attributes. Each generation is essentially independent, leading to variations in facial features, body proportions, clothing details, and styling.

According to research published on arXiv, temporal consistency in generative video models remains an active research challenge, with current architectures struggling to maintain identity across extended sequences.

Real-World Impact

• Narrative storytelling: Creating multi-scene stories with recurring characters becomes nearly impossible
• Brand mascots: Maintaining consistent brand character appearances across marketing videos
• Tutorial content: Host or presenter appearance changes between segments
• Episodic content: Character continuity across series episodes

Current Workarounds

Prompt Strategy for Better Consistency:
"Professional headshot of [detailed character description], 
studio lighting, neutral background, front-facing, 
Reference ID: CHAR001"

Then reference in subsequent prompts:
"Same person as CHAR001, [new scene description]"

While this doesn't guarantee consistency, providing extremely detailed character descriptions and using reference images (when supported) improves results. Some creators generate multiple variations and manually select the most consistent options.

2. Generating Accurate Text and Readable Typography

AI video generators consistently struggle with text generation, producing garbled letters, nonsensical words, and illegible typography. This limitation extends to signs, documents, subtitles, and any on-screen text elements.

The Technical Challenge

According to OpenAI's technical report, text rendering requires precise spatial reasoning and symbolic understanding that current diffusion-based models lack. The models treat text as visual patterns rather than semantic symbols, leading to distorted or meaningless output.

"Text generation in AI video remains fundamentally broken because these models don't understand language as discrete symbols—they see text as textures and shapes, which leads to the characteristic 'AI gibberish' we see in generated content."

Dr. Devi Parikh, Research Director, Meta AI

Common Text Generation Failures

• Misspelled words and letter substitutions
• Inconsistent font sizes and styles within single words
• Warped or distorted letterforms
• Text that changes or morphs between frames
• Incorrect character spacing and alignment

Practical Solutions

1. Post-production overlay: Generate video without text, add typography in editing software
2. Avoid text in prompts: Generate scenes without visible text elements
3. Use video editing tools: Add text overlays using Adobe Premiere or DaVinci Resolve
4. Template approach: Create text templates that overlay AI-generated backgrounds

[Screenshot suggestion: Side-by-side comparison of AI-generated text (garbled) vs. properly overlaid text in post-production]

3. Understanding and Depicting Complex Physics

AI video generators frequently violate basic physics principles, creating impossible movements, incorrect object interactions, and unrealistic material behaviors. Water flows upward, objects pass through solid surfaces, and gravity becomes optional.

Why Physics Breaks Down

Current AI models learn from visual patterns in training data but don't possess underlying physics engines or causal understanding. According to research in Nature Machine Intelligence, generative models lack the structured knowledge of physical laws that would enable realistic simulations.

Common Physics Failures

• Liquid dynamics: Water that doesn't splash, flows in wrong directions, or defies gravity
• Object collisions: Items passing through each other or bouncing unrealistically
• Cloth simulation: Fabric that moves incorrectly or clips through bodies
• Lighting consistency: Shadows that don't match light sources or change illogically
• Motion physics: Acceleration and momentum that violate Newton's laws

Example: Pouring Liquid

Problematic Prompt:
"Person pouring water from bottle into glass"

Result: Water may:
- Float in mid-air
- Flow at wrong angles
- Disappear or multiply
- Ignore glass boundaries

Better Approach:
"Static shot of filled glass with water, 
person's hand holding bottle nearby"

Result: Avoids dynamic liquid simulation entirely

Strategic Workarounds

1. Avoid complex physics scenarios: Choose static or simple motion scenes
2. Use real footage for physics-heavy shots: Combine AI backgrounds with real action
3. Embrace stylization: Use artistic styles where physics violations are acceptable
4. Short clips only: Physics errors compound over time; keep clips under 3-4 seconds

4. Creating Smooth, Natural Hand Movements and Gestures

Hands remain the Achilles' heel of AI video generation. Fingers multiply, merge, bend impossibly, or disappear entirely. Hand-object interactions are particularly problematic, with tools phasing through palms or fingers gripping thin air.

The Hand Problem Explained

Hands are anatomically complex with 27 bones and numerous possible configurations. According to Stability AI's research, the high variability in hand poses combined with frequent occlusion in training data makes hands especially difficult for generative models.

"Hands are the hardest part of the human body for AI to generate accurately because they're incredibly expressive, highly articulated, and constantly in motion. The training data simply doesn't provide enough consistent examples of hands in every possible configuration."

Emad Mostaque, Former CEO, Stability AI

Common Hand Generation Errors

• Wrong number of fingers (usually 6-7, sometimes 3-4)
• Fingers that merge or split mid-gesture
• Impossible joint angles and bending
• Hands that morph or change size between frames
• Objects floating near hands instead of being grasped
• Left/right hand confusion

Practical Avoidance Strategies

Prompt Strategies to Minimize Hand Issues:

✗ Avoid: "Person typing on keyboard"
✓ Better: "Person sitting at desk, hands resting on lap"

✗ Avoid: "Chef chopping vegetables"
✓ Better: "Chef standing in kitchen, arms crossed"

✗ Avoid: "Person pointing at screen"
✓ Better: "Person looking at screen, medium shot"

Key principle: Keep hands:
- Out of frame
- At rest
- In pockets
- Behind back
- Holding large, simple objects

When You Must Show Hands

1. Use wide shots: Hands are less detailed and errors less noticeable
2. Motion blur: Moving hands are harder to scrutinize
3. Partial visibility: Show only part of hands or use strategic framing
4. Generate multiple versions: Create 10-20 variations, select the best
5. Hybrid approach: Use real hand footage composited with AI backgrounds

[Screenshot suggestion: Grid showing common hand generation errors with annotations]

5. Maintaining Temporal Coherence Over Extended Sequences

AI video generators excel at short clips (2-4 seconds) but struggle with longer sequences. Objects drift, backgrounds shift, and the overall scene loses coherence as the video extends beyond a few seconds.

Understanding Temporal Drift

According to Google Research, temporal coherence degrades as video length increases because models lack long-term memory and planning capabilities. Each frame or frame-group is generated with limited context from previous frames, leading to accumulated errors.

Manifestations of Temporal Incoherence

• Background drift: Scenery gradually shifts or morphs
• Object migration: Items slowly move without apparent cause
• Style shift: Visual style or color grading changes mid-clip
• Lighting inconsistency: Light sources or shadows change between frames
• Scale drift: Objects or characters gradually change size

Current Time Limitations by Platform

Platform	Maximum Stable Duration	Recommended Duration
Runway Gen-3	10 seconds	4-5 seconds
Pika Labs	3 seconds	2-3 seconds
Stable Video Diffusion	4 seconds	2-3 seconds
OpenAI Sora (limited access)	60 seconds	10-15 seconds

Strategies for Longer Content

1. Scene-based editing: Generate short clips, edit together with transitions
2. Cut on action: Use natural cutting points to hide temporal inconsistencies
3. B-roll approach: Mix AI clips with real footage or static images
4. Embrace jump cuts: Modern editing styles accommodate frequent cuts
5. Use motion graphics: Transition between AI clips with text or graphics

Video Structure for 60-Second AI Video:

[0-3s]  Opening shot (AI generated)
[3-4s]  Quick transition
[4-7s]  Second angle (AI generated)
[7-8s]  Text overlay transition
[8-11s] Third shot (AI generated)
[11-12s] Graphic element
[12-15s] Fourth shot (AI generated)
...

Total: 15-20 short AI clips edited together
Result: Coherent 60-second video

6. Generating Accurate Lip Sync and Facial Speech

When AI video generators attempt to create speaking characters, the lip movements rarely match the implied speech. Mouths open and close randomly, facial expressions don't align with emotional tone, and the overall effect falls deep into the uncanny valley.

The Lip Sync Challenge

Accurate lip sync requires precise coordination between audio phonemes and visual mouth shapes (visemes). According to recent research on arXiv, current video generation models don't have explicit audio-visual alignment mechanisms, making accurate lip sync nearly impossible.

What Goes Wrong

• Mouth movements that don't match speech timing
• Generic mouth opening without phoneme-specific shapes
• Facial expressions that conflict with speech emotion
• Teeth and tongue movements that appear unnatural
• Head movements that don't align with speech emphasis

Better Alternatives

1. Dedicated lip sync tools: Use Synthesia or HeyGen specifically designed for talking heads
2. Voiceover approach: Generate non-speaking video, add voiceover narration
3. Text-based content: Use on-screen text instead of speaking characters
4. Avoid close-ups: Wide shots make lip sync errors less noticeable
5. Animation style: Stylized animation is more forgiving than photorealism

Prompt Strategy for Non-Speaking Characters:

✗ Avoid: "Person explaining concept to camera"
✓ Better: "Person gesturing thoughtfully, contemplative expression"

✗ Avoid: "News anchor delivering report"
✓ Better: "News anchor sitting at desk, professional pose"

Add voiceover in post-production

7. Understanding and Maintaining Narrative Logic

AI video generators lack understanding of cause and effect, story progression, and narrative coherence. They can't plan multi-shot sequences that tell coherent stories or maintain logical relationships between scenes.

Why Narrative Understanding Fails

According to MIT Technology Review's analysis, AI video models generate content based on pattern matching from training data, not from understanding plot structure, character motivation, or causal relationships. Each generation is essentially isolated from narrative context.

"Current AI video generators are like cinematographers without a script. They can create beautiful individual shots, but they have no concept of the story those shots should tell. That's a fundamental limitation of the technology as it exists today."

Dr. Fei-Fei Li, Professor of Computer Science, Stanford University

Narrative Failures in Practice

• Continuity errors: Objects appear and disappear between scenes
• Spatial inconsistency: Character locations that don't make sense
• Action consequences: Actions without logical results
• Emotional progression: Mood shifts without narrative justification
• Timeline confusion: Time of day or season changes illogically

Workaround: Human-Driven Story Structure

Narrative Planning Template:

1. Write detailed shot list
   Shot 1: [Specific description]
   Shot 2: [Specific description]
   Shot 3: [Specific description]

2. Generate each shot independently
   - Maintain consistent style descriptors
   - Reference previous shots in prompts
   - Generate multiple variations

3. Editorial assembly
   - Arrange shots in narrative order
   - Add transitions for continuity
   - Use audio to bridge gaps
   - Add text for clarity

4. Post-production polish
   - Color grade for consistency
   - Add sound effects for causality
   - Use music to establish mood

Best Practices for Story-Based Content

1. Simple narratives only: Stick to 3-5 shot sequences maximum
2. Explicit transitions: Use fades, wipes, or text cards between AI-generated scenes
3. Voiceover narration: Explain story progression verbally
4. Location-based structure: Keep scenes in single locations
5. Documentary style: Use observational approach rather than plot-driven

8. Handling Precise Camera Movements and Cinematography

While AI video generators can create impressive visuals, they struggle with precise camera control. Smooth tracking shots, specific focal length control, and professional cinematography techniques remain largely out of reach.

Camera Control Limitations

According to Runway's research documentation, current text-to-video models have limited understanding of camera parameters like focal length, aperture, movement speed, and trajectory. Prompts mentioning camera movements often produce unpredictable or incorrect results.

Common Camera Issues

• Inconsistent movement speed: Camera speeds up or slows down randomly
• Shaky or wobbly motion: Lack of smooth stabilization
• Incorrect perspective: Focal length changes mid-shot
• Depth of field errors: Focus shifts illogically or everything stays sharp
• Framing drift: Subject moves out of frame or composition shifts
• Impossible movements: Camera paths that defy physics

Camera Movement Prompt Examples

Low Success Rate Prompts:
"Smooth dolly shot tracking subject"
"360-degree rotating camera around object"
"Crane shot rising from ground level"
"Handheld POV walking through space"

Higher Success Rate Prompts:
"Static wide shot, subject in center"
"Slow zoom in on subject"
"Gentle drift left to right"
"Overhead view looking down"

Key insight: Simple, single-axis movements work better
than complex, multi-axis camera choreography

Achieving Cinematic Results

1. Stick to simple movements: Single-axis pans, tilts, or zooms
2. Use static shots: Let action happen within frame
3. Post-production stabilization: Apply digital stabilization in editing
4. Simulate complex moves: Use editing techniques (whip pans, jump cuts) to fake camera movement
5. Reference specific films: "Shot in the style of [specific cinematographer]" sometimes helps
6. Generate multiple takes: Create 10+ versions, select the smoothest

Cinematography Style Prompts That Work Better

Instead of technical camera terms, use style references:

✓ "Wes Anderson symmetrical composition"
✓ "Roger Deakins natural lighting"
✓ "Emmanuel Lubezki long take style"
✓ "Blade Runner 2049 cinematography"
✓ "Documentary style handheld aesthetic"

These reference established visual styles the model
has seen in training data

[Screenshot suggestion: Comparison of requested vs. actual camera movement in AI-generated video]

Tips & Best Practices for Working Within AI Video Limitations

General Strategy Guidelines

1. Embrace the medium's strengths: Focus on what AI does well (environments, textures, abstract visuals)
2. Plan for limitations: Design projects that avoid problem areas
3. Hybrid workflows: Combine AI-generated content with traditional footage
4. Iteration is essential: Generate 20-50 variations to find usable clips
5. Short is better: Keep individual clips under 4 seconds
6. Style over realism: Stylized or abstract approaches hide technical limitations

Prompt Engineering Best Practices

Effective Prompt Structure:

[Style/Aesthetic] + [Subject/Action] + [Environment] + 
[Lighting] + [Camera Info] + [Quality Tags]

Example:
"Cinematic wide shot, person walking through misty forest,
golden hour lighting, shallow depth of field, 
4K, professional color grading"

Avoid:
- Complex actions
- Multiple subjects interacting
- Precise timing requirements
- Text or readable elements
- Close-ups of hands or faces
- Long duration requests

Quality Control Checklist

• ☐ Watch at full resolution for artifacts
• ☐ Check frame-by-frame for consistency
• ☐ Verify no text gibberish is visible
• ☐ Confirm physics look plausible
• ☐ Check hands/faces for errors
• ☐ Ensure no morphing or warping
• ☐ Verify lighting consistency
• ☐ Check for temporal coherence

Post-Production Enhancement

1. Color grading: Unify look across multiple AI clips
2. Speed ramping: Slow down or speed up to hide artifacts
3. Strategic cropping: Remove problematic edges or elements
4. Overlay elements: Add text, graphics, or effects in post
5. Audio design: Strong sound design compensates for visual limitations
6. Transitions: Use creative transitions to hide inconsistencies

Common Issues & Troubleshooting

Issue: Generated Video Looks "AI-ish" or Uncanny

Symptoms: Smooth, overly perfect textures; weird morphing; unnatural movements

Solutions:

• Add film grain or noise in post-production
• Apply subtle camera shake
• Use color grading to add imperfections
• Mix with real footage for reference
• Choose stylized rather than photorealistic prompts

Issue: Results Don't Match Prompt

Symptoms: AI interprets prompt differently than intended

Solutions:

• Simplify prompt language
• Add specific style references
• Use negative prompts (if supported)
• Try alternative phrasings
• Reference specific artists or films
• Generate many variations

Issue: Video Has Obvious Artifacts or Glitches

Symptoms: Flickering, warping, sudden changes

Solutions:

• Reduce requested duration
• Simplify scene complexity
• Use higher quality settings
• Try different seed values
• Apply temporal smoothing in post
• Use video stabilization tools

Issue: Can't Get Consistent Style Across Multiple Clips

Symptoms: Each clip looks different despite similar prompts

Solutions:

• Use identical style descriptors in all prompts
• Reference the same artist/film for each generation
• Use style reference images (if supported)
• Apply uniform color grading in post
• Generate all clips in single session
• Use same seed/settings across generations

The Future: What's Coming Next

While current AI video generators have significant limitations, rapid progress continues. According to Anthropic's research updates, several promising developments are on the horizon:

Emerging Solutions

• Multi-modal models: Systems that understand both video and audio for better lip sync
• Physics-informed generation: Models trained with physics engines for realistic interactions
• Longer context windows: Improved temporal coherence across extended sequences
• Character consistency: Dedicated systems for maintaining identity across shots
• Precision controls: Better camera and movement specification tools

Timeline Expectations

Based on current research trajectories and expert predictions:

• 2025: Improved character consistency, better hand generation
• 2026: Reliable text generation, extended temporal coherence
• 2027: Physics-aware generation, precise camera control
• 2028+: True narrative understanding, production-ready quality

"We're in the early days of AI video generation. The limitations we see today will seem quaint in just a few years, but understanding them now helps us build better tools and workflows for the future."

Dr. Jiajun Wu, Assistant Professor of Computer Science, Stanford University

Conclusion: Working Smart with AI Video Tools

AI video generators in 2025 are powerful tools with clear limitations. Success comes from understanding what they can't do well and planning projects accordingly. The eight limitations we've explored—character consistency, text generation, physics understanding, hand movements, temporal coherence, lip sync, narrative logic, and camera control—aren't insurmountable obstacles. They're design constraints that shape how we approach AI-assisted video production.

Key Takeaways

• Design projects around AI strengths, not weaknesses
• Use short clips (2-4 seconds) for best results
• Plan for extensive post-production and editing
• Generate many variations, select the best
• Combine AI content with traditional techniques
• Set realistic expectations with clients and stakeholders

Next Steps

1. Experiment with different platforms: Try Runway, Pika, and others to find what works best for your needs
2. Build a workflow: Develop a repeatable process for generation, selection, and editing
3. Create a style guide: Document successful prompts and techniques
4. Join communities: Engage with other creators to learn best practices
5. Stay updated: Follow AI video research and new tool releases

By understanding these limitations and working strategically within them, you can create compelling video content that leverages AI's strengths while avoiding its weaknesses. The technology will improve, but the principles of good planning, creative problem-solving, and quality control remain timeless.

Frequently Asked Questions

Which AI video generator handles these limitations best?

As of 2025, OpenAI's Sora (limited access) shows the best overall performance, particularly for temporal coherence and physics. Runway Gen-3 offers the best balance of quality and accessibility. However, all platforms struggle with the eight limitations discussed—choose based on your specific use case rather than expecting any single tool to solve all problems.

Can I use AI-generated videos commercially?

Most platforms allow commercial use, but check specific terms of service. Key considerations: ensure you have rights to any reference images used, verify the platform's copyright policy, and be transparent about AI-generated content when required by platform policies or regulations. Always review the latest terms on each platform's website.

How much post-production is typically needed?

Expect significant post-production for professional results. Budget 3-5 hours of editing for every minute of final AI-generated video content. This includes: selecting the best clips from multiple generations, color grading, adding transitions, incorporating text/graphics, audio design, and quality control.

Will these limitations be fixed soon?

Some limitations (character consistency, text generation) will likely see significant improvements within 12-18 months. Others (complex physics, narrative understanding) represent fundamental challenges that may take 3-5 years to solve. Plan current projects around existing limitations rather than waiting for future improvements.

What's the best way to learn AI video generation?

Start with free tiers of platforms like Pika Labs or Stable Video Diffusion. Focus on simple projects first, document successful prompts, and gradually increase complexity. Join communities like the Stable Diffusion subreddit or Discord servers for specific platforms to learn from experienced users.

References

1. OpenAI - Sora: Creating Video from Text
2. Runway - Gen-3 Alpha
3. Pika Labs - AI Video Generation
4. Meta AI - Generative AI Text-to-Video Research
5. arXiv - Temporal Consistency in Video Generation Models
6. OpenAI - Video Generation Models as World Simulators
7. Nature Machine Intelligence - Physics Understanding in AI Systems
8. Stability AI - Stable Video Diffusion Research
9. Google Research - Temporal Coherence in Video Models
10. arXiv - Audio-Visual Alignment in Generative Models
11. Synthesia - AI Video Generation Platform
12. HeyGen - AI Video Generator
13. MIT Technology Review - OpenAI's Sora Analysis
14. Runway Research - Video Generation Documentation
15. Anthropic - AI Research Updates
16. Adobe Premiere Pro
17. DaVinci Resolve
18. Hugging Face - Stable Video Diffusion
19. Reddit - Stable Diffusion Community

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Cover image: AI generated image by Google Imagen