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AMASS

A unified collection of human motion-capture datasets represented with a common body model.

About

AMASS is a public research dataset from Max Planck Institute for Intelligent Systems. A unified collection of human motion-capture datasets represented with a common body model.

Start with a representative slice and inspect its Body pose, Motion capture, Metadata signals together. This makes schema, timing, calibration, and annotation issues visible before the dataset becomes part of a larger training or evaluation pipeline.

Suggested agent workflows

Copy a prompt into your agent of choice. Each workflow starts small, respects the source terms, and makes the result inspectable in Rerun.

Audit a training slice

Fetch AMASS from its official source at https://amass.is.tue.mpg.de/. Read and preserve its source terms and citation requirements. Build a reproducible manifest for a representative slice, then load it into Rerun. Inspect the Body pose, Motion capture, Metadata streams for timestamp gaps, missing samples, calibration problems, and annotation inconsistencies. Summarize the findings and save both the validation script and a Rerun recording that makes every issue easy to review.

Build a baseline pipeline

Use AMASS to scaffold a baseline pipeline for Motion synthesis, Human pose. Start with a small deterministic cohort and document the selection criteria. Create the dataset adapter, preprocessing steps, train and validation splits, and one lightweight baseline. Log inputs, labels, predictions, losses, and representative failures to Rerun so I can inspect the full experiment timeline. Include exact setup and reproduction commands.

Find useful failure cohorts

Analyze AMASS and propose a practical cohort taxonomy for Motion synthesis, Human pose. Use available metadata and the Body pose, Motion capture, Metadata signals to identify hard cases, rare conditions, coverage gaps, and possible leakage between splits. Materialize the cohorts as queryable metadata, log examples from each cohort to Rerun, and produce a short report explaining which cohorts should become regression tests. Do not infer labels that are not present in the source data.