Video generation

So wird trainiert

Objectives include spatiotemporal denoising in pixel or latent space and next-token prediction over visual codes. Conditioning can include text, key frames, motion, masks, or camera controls.

So läuft die Inferenz

The system samples a clip jointly or in windows, often with cascaded refinement. Longer duration, higher resolution, and stronger temporal coherence all increase compute and memory pressure.

Stärken

• •Text- or image-conditioned motion synthesis
• •Video extension, interpolation, and editing
• •Latent representations can amortize the cost of raw pixels

Zielkonflikte

• •Identity and object persistence can drift over time
• •Generation and human review are expensive
• •Physics, timing, readable text, and synchronized audio require dedicated tests

Geeignet, wenn

• Short creative clips or controlled transformations
• Temporal failure modes are included in the acceptance rubric
• Asynchronous generation is acceptable

Vermeiden oder hinterfragen, wenn

• A real-time deterministic renderer is required
• Continuity or physical correctness cannot be reviewed
• The workflow cannot support provenance and consent controls