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We introduce MUGL, a novel deep neural model for large-scale, diversegeneration of single and multi-person pose-based action sequences withlocomotion. Our controllable approach enables variable-length generationscustomizable by action category, across more than 100 categories. To enableintra/inter-category diversity, we model the latent generative space using aConditional Gaussian Mixture Variational Autoencoder. To enable realisticgeneration of actions involving locomotion, we decouple local pose and globaltrajectory components of the action sequence. We incorporate duration-awarefeature representations to enable variable-length sequence generation. We use ahybrid pose sequence representation with 3D pose sequences sourced from videosand 3D Kinect-based sequences of NTU-RGBD-120. To enable principled comparisonof generation quality, we employ suitably modified strong baselines duringevaluation. Although smaller and simpler compared to baselines, MUGL providesbetter quality generations, paving the way for practical and controllablelarge-scale human action generation.

MUGL: Large Scale Multi Person Conditional Action Generation with Locomotion