Add Vive teleop MuJoCo visualization examples#688
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Two MuJoCo passive-viewer samples that consume IsaacTeleop's HeadTracker / ControllerTracker / HandTracker streams (typically arriving from a Vive headset via CloudXR): * visualize_poses_mujoco_example.py - HMD + L/R controller as textured mocap bodies; free camera auto-recentres on the head so mouse-wheel zoom always converges on the action. * visualize_hands_mujoco_example.py - both hands as 26-joint skeletons drawn into viewer.user_scn each frame (no extra mocap bodies needed), with the HMD as optional context. Both convert OpenXR's Y-up frame to MuJoCo's Z-up and reorder quaternions from xyzw to wxyz. Per-device mesh-frame rotations align the OBJ local frames to the OpenXR pose frame; values are tunable constants at the top of each script. Bundled assets (visualize_poses_assets/, ~5.7 MB) are the generic HMD and Vive Focus 3 controllers with their *_color.png textures. Each sample degrades gracefully to coloured primitive boxes if assets are missing. pyproject.toml declares mujoco / numpy / scipy so 'uv run' fetches them on first launch.
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Description
Two MuJoCo passive-viewer samples that consume IsaacTeleop's HeadTracker / ControllerTracker / HandTracker streams (typically arriving from a Vive headset via CloudXR):
Both convert OpenXR's Y-up frame to MuJoCo's Z-up and reorder quaternions from xyzw to wxyz. Per-device mesh-frame rotations align the OBJ local frames to the OpenXR pose frame; values are tunable constants at the top of each script.
Bundled assets (visualize_poses_assets/, ~5.7 MB) are the generic HMD and Vive Focus 3 controllers with their *_color.png textures. Each sample degrades gracefully to coloured primitive boxes if assets are missing.
pyproject.toml declares mujoco / numpy / scipy so 'uv run' fetches them on first launch.
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