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![MUGL Main Image](/static/img/mugl/cover_image.png)
Abstract
- We introduce MUGL, a novel deep neural model for large-scale, diverse generation of single and multi-person pose-based action sequences with locomotion.
- Our controllable approach enables variable-length generations customizable by action category, across more than 100 categories.
- To enable intra/inter-category diversity, we model the latent generative space as a Conditional Gaussian Mixture Variational Autoencoder.
- To enable realistic generation of actions involving locomotion, we decouple local pose and global trajectory components of the action sequence. We incorporate duration-aware feature representations to enable variable-length sequence generation.
- We use a hybrid pose sequence representation with 3D pose sequences sourced from videos and 3D Kinect-based sequences of NTU-RGBD-120.
- To enable principled comparison of generation quality, we employ suitably modified strong baselines during evaluation. Although smaller and simpler compared to baselines, MUGL outperforms the baselines across multiple generative model metrics.
- Code will be made available.
Architecture
![Architecture](/static/img/mugl/Architecture.png)
Generated Samples
![MUGL Quantitative Comparision](/static/gifs/MUGL/generations.gif)
Quantitative Comparision
![MUGL Quantitative Comparision](/static/img/mugl/quant_compare.jpeg)
Qualitative Comparision
Issues with feature based generative quality measures
![Issues with feature based generative quality measures](/static/gifs/MUGL/FeatureBasedGenerativeQualityMeasures.gif)
Citation
Please cite our paper if you end up using it for your own research.
@InProceedings{Maheshwari_2022_WACV, author = {Maheshwari, Shubh and Gupta, Debtanu and Sarvadevabhatla, Ravi Kiran}, title = {MUGL: Large Scale Multi Person Conditional Action Generation With Locomotion}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {257-265} }