✨Unified Number-Free Text-to-Motion Generation Via Flow Matching

📝 Summary:
Existing text-to-motion models struggle with variable agents, leading to inefficiency and errors. This paper proposes Unified Motion Flow UMF, a two-stage approach prior and reaction that uses P-Flow and S-Flow in a unified latent space. UMF effectively generates multi-person motion from text, mi...

🔹 Publication Date: Published on Mar 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.27040
• PDF: https://arxiv.org/pdf/2603.27040
• Project Page: https://githubhgh.github.io/umf/
• Github: https://github.com/Githubhgh/UMF_CVPR

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For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#TextToMotion #FlowMatching #GenerativeAI #MotionSynthesis #DeepLearning

✨HandX: Scaling Bimanual Motion and Interaction Generation

📝 Summary:
HandX presents a new foundation for bimanual hand motion synthesis, offering a high-fidelity dataset, an LLM-driven annotation method, and new evaluation metrics. It enables high-quality dexterous motion generation, with scaling trends observed.

🔹 Publication Date: Published on Mar 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.28766
• PDF: https://arxiv.org/pdf/2603.28766
• Project Page: https://github.com/handx-project/HandX
• Github: https://github.com/handx-project/HandX

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For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#MotionSynthesis #BimanualInteraction #DexterousManipulation #AIResearch #LLM