How Poser turns ski video into technique feedback

1. Standardize the clip

2. Find the primary skier

3. Track the skier frame by frame

4. Estimate 3D body pose and smoothen movements

5. Detect turns, calculate metrics, and build outputs

After upload, Poser trims the clip to the useful section and standardizes the video so the rest of the analysis pipeline works from a consistent input.

Clear footage still matters. The best clips keep the skier large enough in frame, show several linked turns, and avoid heavy occlusion from other skiers or background movement.

Before Poser can calculate anything about movement, it needs to know which person in the video is the skier to analyze. It scans through the clip and identifies the primary skier across the run.

This is simple when one skier is clearly visible, but real ski footage often includes people crossing in front, lift traffic, or background skiers. The primary-skier step gives the rest of the pipeline a single target to follow.

Once Poser knows who to follow, it tracks exactly where that skier is in every frame. At this stage, the system builds a segmentation mask: a clear outline of the skier's body and equipment against the rest of the video.

That frame-by-frame tracking is the foundation for the visual outputs. It is what lets Poser isolate the skier, keep the skier centered in replay views, and pass a clean movement target into pose estimation.

With the skier tracked, Poser estimates 3D body position for each frame. In practical terms, it infers where the major body joints are over time so the skier's stance and movement can be represented as a moving body model.

Raw AI pose results can contain small jumps or jitter from frame to frame, so Poser smooths the estimated body motion over time. The goal is a stable 3D movement signal that follows the skier without amplifying model noise.

Turn detection comes first because most ski metrics depend on turn timing. Once Poser knows where each turn starts and ends, it can calculate movement observations such as edge similarity, angulation, and center-of-gravity placement in the right phase of the turn.

Finally, Poser creates the outputs you can review in the frontend: skeleton overlays projected back onto the video, head-tracked replay views, turn data, and metric displays that make the analysis easier to inspect.