피파 한 줄 정리: Image generation은 2024년에 production 품질에 도달했고, video는 2026년에 거의 따라잡았어. 'video는 왜 더 어려운가'는 단순히 'pixel이 더 많아서'가 아니라 *temporal coherence*라는 다른 문제야.
By 2024, AI image generation was already producing stunning, production-quality results. Video generation, while improving rapidly, is still catching up. Why? It's not just "video is bigger." The difficulty gap is fundamentally architectural.
Here's the analogy: image generation is like painting a single masterpiece. Video generation is like directing a play. A painting needs to look great frozen in time. A play needs every actor to move consistently, maintain their identity, remember their blocking, react to each other, obey physics, and do all of this across hundreds of moments — any one of which could break the illusion.
The Three Multipliers of Difficulty
1. Temporal Consistency
In a video, a person's face must look the same in frame 1 and frame 120. Their shirt can't change color. The background can't rearrange itself. Current research shows that even the best models (Sora 2, Kling 3.0, Veo 3.1) still struggle with character consistency across longer clips, often requiring 15-20 regenerations per scene to get acceptable results.
Image: Just make this ONE frame look good
Frame 47: ✅ Perfect
Video: Make ALL frames look good AND consistent with each other
Frame 1: ✅ Frame 2: ✅ Frame 3: ✅ ... Frame 47: 😰 Frame 48: ❌ drift!
2. Motion Coherence
Objects need to move in physically plausible ways. A walking person's legs need to cycle naturally, their hair needs to bounce with each step, their shadow needs to move with them. The model must learn physics-like patterns — not just "what things look like" but "how things move." This is a vastly larger pattern space than static appearance.
3. Compute Scale
A 1024×1024 image has ~3 million pixel values. A 5-second video at 24fps at the same resolution has ~360 million values — roughly 120× more data. And those values aren't independent — they have complex temporal relationships. The compute required for video generation is orders of magnitude greater than for image generation.
The Current State (2025-2026)
Video generation has made remarkable progress:
- Sora 2 produces cinematic quality with natural camera movement (60-sec clips)
- Veo 3.1 leads in physics accuracy and generates synchronized audio natively
- Kling 3.0 offers 4K output with strong physical realism at budget-friendly pricing
- Runway Gen-4 excels at temporal consistency for professional post-production
Finger artifacts and basic motion issues are largely solved. But long-range character consistency, complex multi-character interactions, and coherent sequences beyond 30-60 seconds remain active challenges. The field is roughly where image generation was in 2022-2023 — impressive demos, but workflows still require heavy curation and editing.
- Video generation is fundamentally harder due to temporal consistency, motion coherence, and compute scale.
- Image = one perfect moment. Video = maintaining coherence across hundreds of dependent frames.
- Video models have improved dramatically (2025-2026) but still require heavy curation for production work.
- Practical video workflows generate short clips and edit them together, rather than trying for one perfect long sequence.