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Existing 3D human manipulation datasets are valuable, but are limited in scale and diversity. At #CVPR2025, we will introduce GigaHands👐 which, to our knowledge, is the most extensive 3D bimanual manipulation, interaction, and gesture dataset.🧵👇(1/9)
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Some statistics about GigaHands 📊 🕒 34 hrs of manual activities 👥 56 participants 🧱 417 objects 🎞️ 14k 3D motion sequences 🖼️ 183M frames 📝 84k text annotations We hope the dataset opens new research directions in AI, robotics, computer vision, and animation! 🤖🎬🖥️ (2/9)

🛠️ How did we build GigaHands? ✅ Markerless multi-camera motion capture with 51 cameras ✅ Procedurally-generated instructions ensuring comprehensive coverage of bimanual interactions ✅ Rich data captured without intrusive markers! (3/9)

📑 It has rich annotations for: ✋ 3D hand shape & pose 📦 3D rigid object shape & pose; object scans 🧩 Hand-object segmentation masks 📍 2D/3D keypoints 🎥 Camera poses 📝 Detailed textual descriptions All captured using a markerless, multi-camera setup! 📸(4/9)

GigaHands enables applications like text-conditioned motion generation, i.e., describe actions in text 👉 Generate realistic hand interactions! 🤝✨ (5/9)

Or motion captioning to generate textual descriptions from hand motions—even across diverse datasets! 📖🔄 (6/9)

Since GigaHands has dense cameras, we can reconstruct dynamic 3D hand-object interactions in great detail 🛠️🔍 (7/9)

Tracking data from GigaHands can also enable motion retargeting to robotic and virtual hands. 🤖🖐️ (8/9)

This work was led by my PhD student, @RaoFu79761158 in collaboration with Dingxi Zhang, Alex Jiang, Wanjia Fu, Austin Funk, and Daniel Ritchie @BrownVisualComp. Paper, data and code released: 🌐 #GigaHands #CVPR2025 #AI #ComputerVision #Robotics (9/9)

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Nice work! This will be a useful dataset!

Thanks, Michael!
