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license: apache-2.0 |
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--- |
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README.md |
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-------- |
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Step 1: Core Architecture Design |
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The model combines: |
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Hierarchical Video Encoder (V-JEPA inspired) |
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Contextual Text Encoder (LLM-based) |
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Joint Embedding Space |
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Diffusion-Based Decoder |
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### Key Components: |
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1. **Cognitive Hierarchy:** |
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- Video encoder extracts spatiotemporal features at multiple scales |
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- Text encoder provides semantic context |
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- Fusion transformer establishes cross-modal relationships |
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2. **Diffusion-Based Prediction:** |
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- Conditional UNet generates future frames |
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- Training via masked future prediction |
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3. **Contextual Reasoning:** |
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- Joint embedding space enables multimodal understanding |
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- Temporal coherence through video-text alignment |
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### Requirements: |
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- PyTorch 2.0+ |
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- Hugging Face Transformers |
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- Diffusers library |
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- CUDA 11.7+ |
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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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