✨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
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#TextToMotion #FlowMatching #GenerativeAI #MotionSynthesis #DeepLearning
📝 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
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#TextToMotion #FlowMatching #GenerativeAI #MotionSynthesis #DeepLearning