Architectures de modèles
Video generation
Generate spatial and temporal structure together using frame, patch, or latent video representations with cross-frame computation.
Modèle mental
Image generation plus a time axis, and therefore a much larger consistency, memory, motion, and evaluation problem.
Flux de données
- Text, image, or video condition
- Spatiotemporal representation
- Temporal diffusion or token generator
- Video decoder / upsampler
- Frames + audio alignment checks
Entraînement
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.
Exécution de l’inférence
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.
Atouts
- Text- or image-conditioned motion synthesis
- Video extension, interpolation, and editing
- Latent representations can amortize the cost of raw pixels
Compromis
- Identity and object persistence can drift over time
- Generation and human review are expensive
- Physics, timing, readable text, and synchronized audio require dedicated tests
À utiliser lorsque
- Short creative clips or controlled transformations
- Temporal failure modes are included in the acceptance rubric
- Asynchronous generation is acceptable
À éviter ou remettre en question lorsque
- A real-time deterministic renderer is required
- Continuity or physical correctness cannot be reviewed
- The workflow cannot support provenance and consent controls
Familles publiées à titre d’exemple
- • Video Diffusion Models research family
- • Latent video diffusion and token-based video generators
Souvent combinée avec
Latent diffusionDiffusion TransformerAutoregressive media modelsMultimodal fusion