#NVIDIA just released a whole ecosystem for human(oid) motion and robot learning from human data. 🚀🦾 Data, as we all know, is the key to scaling AI models. To accelerate the field of Embodied AI, we have open-sourced a full stack of models and tools to capture, generate, retarget, and simulate human(oid) motion data at scale, along with a massive high-quality dataset and a standard human skeletal representation, SOMA, to make them all seamlessly communicate with each other. The entire suite is available under the Apache 2.0 license. 1️⃣ SOMA: A universal interface to unify all parametric human body models (SOMA-shape, SMPL, MHR, etc.) into a standard skeletal representation, eliminating the need for custom adapters or model-specific retargeting. 🔗 lnkd.in/gsxhiJnn 2️⃣ Kimodo: High-fidelity, controllable text-to-motion generation for both humans and humanoid robots. 🔗 lnkd.in/gCc84XnX 3️⃣ GEM: A global human pose estimation method from in-the-wild videos, natively compatible with SOMA. 🔗 lnkd.in/g_QAvRjn 4️⃣ Bones-SEED: A massive dataset of 150k+ motions in SOMA format, including data already retargeted for the Unitree G1, created with our partners at Bones Studio. 🔗 lnkd.in/gfx-QD-w 🔗 lnkd.in/gyNdTwQx 5️⃣ SOMA Retargeter: A dedicated tool for seamless motion retargeting from the SOMA skeleton to the Unitree G1. 🔗 lnkd.in/gqz9Na-H 6️⃣ ProtoMotions: Our high-performance simulation framework for training digital human(oid)s via RL, now with native SOMA support. 🔗 lnkd.in/gmvMikMU This is just the beginning, and we have much more in the pipeline. Excited to see what the community builds next! #NVIDIA #GTC #GTC2026 #Robotics #EmbodiedAI #PhysicalAI @NVIDIAAI
