Retrieval from motion datasets combined with LLM task parsing and reward-guided noise initialization enables training-free diffusion optimization to satisfy severe spatiotemporal constraints in human motion generation.
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MPDiT uses a hierarchical multi-patch design in transformers to lower computation in diffusion models by handling coarse global features first then fine local details, plus faster-converging embeddings.
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Towards Highly-Constrained Human Motion Generation with Retrieval-Guided Diffusion Noise Optimization
Retrieval from motion datasets combined with LLM task parsing and reward-guided noise initialization enables training-free diffusion optimization to satisfy severe spatiotemporal constraints in human motion generation.
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MPDiT: Multi-Patch Global-to-Local Transformer Architecture For Efficient Flow Matching and Diffusion Model
MPDiT uses a hierarchical multi-patch design in transformers to lower computation in diffusion models by handling coarse global features first then fine local details, plus faster-converging embeddings.