Add teaser video and figures to card

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/teaser.mp4 filter=lfs diff=lfs merge=lfs -text
+assets/teaser_fig.png filter=lfs diff=lfs merge=lfs -text
+assets/pipeline.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -20,6 +20,12 @@ datasets:
 
 [Paper](https://arxiv.org/abs/2610.25988) | [Project Page](https://ssio-imu.github.io) | [Code](https://github.com/amazon-far/SSIO) | [Collection](https://huggingface.co/collections/Open-IO/ssio-self-supervised-inertial-odometry-6ad670b4bf71f392cd23d1c8)
 
+<div align="center">
+<video autoplay loop muted playsinline controls width="80%">
+  <source src="https://huggingface.co/Open-IO/SSIO/resolve/main/assets/teaser.mp4" type="video/mp4">
+</video>
+</div>
+
 Every model in the tables of **[SSIO](https://ssio-imu.github.io)**, which trains inertial
 odometry from a robot's own unlabeled IMU stream, with no velocity or position labels.
 SSIO trains in two steps: **Integration Self-Supervision**, where the network learns
@@ -27,6 +33,10 @@ body-frame velocity from an IMU integration teacher on short windows ("Ours"), a
 **Pose Graph Distillation**, which fine-tunes it on sequence-consistent velocities from a
 pose graph ("Ours (w/ PGD)"). The supervised AirIO baselines are released alongside.
 
+![SSIO (orange) vs. supervised AirIO (blue): trajectories and average velocity error on six embodiments](assets/teaser_fig.png)
+
+![SSIO pipeline: integration self-supervision from a frozen IMU integration teacher, then optional pose graph distillation](assets/pipeline.png)
+
 ## Checkpoints
 
 One directory per table cell, `<dataset>/<recipe>/`, named after the recipe in

assets/teaser.mp4

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