ProMoDD: Dataset Distillation for 3D Human Motion via Prototype-Conditioned Diffusion Models

Key points

• Dataset distillation (DD) aims to retain the training utility of a large dataset in a much smaller set of examples.1
• For skeleton-based action recognition, this requires capturing discriminative motion variation without disrupting the geometry and temporal continuity of articulated bodies.1
• We further tailor diffusion training to motion data, enabling the generator to model both skeletal configurations and their temporal evolution.1
• We then construct each compact training set through budget-aware hybrid selection: representative real sequences cover densely populated regions of the pooled recognition space, while selected generated candidates complement them in less-populated regions.1

Sources (1)

• [1]ProMoDD: Dataset Distillation for 3D Human Motion via Prototype-Conditioned Diffusion Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 03:50 AM

  We propose Prototype-Conditioned Motion Dataset Distillation (ProMoDD), which conditions a motion diffusion model on class-wise prototypes of temporally resolved recognition features.

  Dataset distillation (DD) aims to retain the training utility of a large dataset in a much smaller set of examples.

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