README.md
Step 1: Core Architecture Design The model combines:
Hierarchical Video Encoder (V-JEPA inspired)
Contextual Text Encoder (LLM-based)
Joint Embedding Space
Diffusion-Based Decoder
Key Components:
Cognitive Hierarchy:
- Video encoder extracts spatiotemporal features at multiple scales
- Text encoder provides semantic context
- Fusion transformer establishes cross-modal relationships
Diffusion-Based Prediction:
- Conditional UNet generates future frames
- Training via masked future prediction
Contextual Reasoning:
- Joint embedding space enables multimodal understanding
- Temporal coherence through video-text alignment
Requirements:
- PyTorch 2.0+
- Hugging Face Transformers
- Diffusers library
- CUDA 11.7+
This architecture provides a foundation for building world models that understand temporal dynamics and contextual relationships through multimodal fusion and generative prediction.
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