Video generation

训练方式

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.

推理运行方式

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.

优势

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

权衡

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

适用场景

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

应避免或质疑的场景

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