モデルアーキテクチャ
Video generation
Generate spatial and temporal structure together using frame, patch, or latent video representations with cross-frame computation.
理解のためのモデル
Image generation plus a time axis, and therefore a much larger consistency, memory, motion, and evaluation problem.
データフロー
- Text, image, or video condition
- Spatiotemporal representation
- Temporal diffusion or token generator
- Video decoder / upsampler
- Frames + audio alignment checks
学習方法
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.
推論の実行方法
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.
強み
- Text- or image-conditioned motion synthesis
- Video extension, interpolation, and editing
- Latent representations can amortize the cost of raw pixels
トレードオフ
- Identity and object persistence can drift over time
- Generation and human review are expensive
- Physics, timing, readable text, and synchronized audio require dedicated tests
適する場合
- Short creative clips or controlled transformations
- Temporal failure modes are included in the acceptance rubric
- Asynchronous generation is acceptable
避ける・再検討する場合
- A real-time deterministic renderer is required
- Continuity or physical correctness cannot be reviewed
- The workflow cannot support provenance and consent controls
公開された方式の例
- • Video Diffusion Models research family
- • Latent video diffusion and token-based video generators
よく組み合わせる要素
Latent diffusionDiffusion TransformerAutoregressive media modelsMultimodal fusion