Refresh hero and per-scenario clips from Cosmos3 paper sources; drop static modalities grid

README.md

@@ -78,25 +78,25 @@ Each scenario stages a different self-contained event inside a shared warehouse
 
 A worker stands at a fixed location while a forklift navigates along a planned path toward the same location. A configurable last-moment dodge distance distinguishes a near-miss from a direct-contact event, so the same scene composition can produce both event classes by varying a single parameter. Each multi-camera run is captured by a mixture of ceiling-mounted CCTV-style cameras (camera aliases `ceiling_00` through `ceiling_04`) and worker-height eye-level cameras (`eye_00` through `eye_04`).
 
-![Forklift–human near-miss — full 10-second run from a ceiling camera.](./assets/clip_nearmiss.webp)
+![Forklift–human near-miss — full 10-second run shown across all five modalities (left to right: RGB, depth, instance segmentation, shaded segmentation, Canny edges).](./assets/clip_nearmiss.webp)
 
 ### Warehouse fire
 
 A localized volumetric fire ignites at a randomized position and time while a small crew of workers performs random walks. On ignition, each worker reacts: it orients toward the flame and then runs toward a designated exit waypoint along a navigation-mesh path. The result is rare emergency-response footage that combines dynamic flames, smoke, and coordinated human evacuation in a single shot. Cameras are placed at ceiling height to maximize floor coverage, with aliases `ceiling_00` through `ceiling_04`.
 
-![Warehouse fire — full 10-second run, ignition followed by coordinated evacuation.](./assets/clip_fire.webp)
+![Warehouse fire — full 10-second run, ignition followed by coordinated evacuation, shown across all five modalities (left to right: RGB, depth, instance segmentation, shaded segmentation, Canny edges).](./assets/clip_fire.webp)
 
 ### Forklift–shelf collision
 
 A forklift drives at a parameterized initial distance toward a populated storage shelf and impacts it, producing visible rigid-body knock-over and debris dynamics. An optional character can be placed along the forklift's path to extend the scenario to a three-body forklift–shelf–human event. Cameras are placed circularly around the impact site at varying heights, distances, and look-down angles, with aliases `cam_00` through `cam_05`.
 
-![Forklift–shelf collision — full 15-second run, drive into the shelf and ensuing debris.](./assets/clip_forklift_collision.webp)
+![Forklift–shelf collision — full 15-second run, drive into the shelf and ensuing debris, shown across all five modalities (left to right: RGB, depth, instance segmentation, shaded segmentation, Canny edges).](./assets/clip_forklift_collision.webp)
 
 ### Warehouse box pickup
 
 A worker navigates to a randomly placed box, performs a contact-rich pickup motion, and carries the box through the warehouse. This scenario provides routine, non-incident action coverage as a counterpoint to the three safety scenarios. The camera rig is a mixed CCTV and eye-level configuration, with aliases `cam_00` through `cam_09`.
 
-![Warehouse box pickup — full 15-second run, walk plus contact-rich pickup plus carry.](./assets/clip_box_pickup.webp)
+![Warehouse box pickup — full 15-second run, walk plus contact-rich pickup plus carry, shown across all five modalities (left to right: RGB, depth, instance segmentation, shaded segmentation, Canny edges).](./assets/clip_box_pickup.webp)
 
 ## Multi-view coverage
 
@@ -108,9 +108,7 @@ For the fire scenario, the rig is the five ceiling cameras only. For the forklif
 
 ## Ground-truth modalities
 
-The synthetic origin of the dataset gives us access to deterministic, perfectly-aligned ground truth for every frame, rendered directly by the simulator rather than predicted by a model. The figure below shows, for a single representative frame from each scenario, the RGB video together with the four annotation modalities that are visible as imagery: log-normalized colorized metric depth, instance segmentation (colorized so the per-pixel identity is visible), shaded segmentation (the same per-pixel identity rendered with normal-based shading), and a Canny edge map computed on the shaded segmentation.
-
-![Ground-truth modalities — one frame per scenario across RGB, depth, instance segmentation, shaded segmentation, and Canny edges.](./assets/modalities_grid.png)
+The synthetic origin of the dataset gives us access to deterministic, perfectly-aligned ground truth for every frame, rendered directly by the simulator rather than predicted by a model. The per-scenario animations above show, alongside the photoreal RGB video, the four annotation modalities that are visible as imagery: log-normalized colorized metric depth, instance segmentation (colorized so the per-pixel identity is visible), shaded segmentation (the same per-pixel identity rendered with normal-based shading), and a Canny edge map computed on the shaded segmentation. Because all five modalities are produced by the same simulator step from the same camera, they are pixel-aligned across every frame.
 
 In addition to the imagery shown above, every frame ships with per-agent two-dimensional axis-aligned bounding boxes (both tight and loose), per-agent oriented three-dimensional bounding boxes, and the camera intrinsics and extrinsics that produced the frame. These structured annotations live in per-camera consolidated JSON files in the upcoming artifacts tier.