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Human Motion Forecasting (Generation) with RNNs

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"On human motion prediction using recurrent neural networks", Julieta Martinez, Michael J. Black, Javier Romero. CVPR2017
https://arxiv.org/abs/1705.02445

Published in: Engineering
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Human Motion Forecasting (Generation) with RNNs

  1. 1. Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry T. Um ON HUMAN MOTION PREDICTION USING RNNS (2017) 1
  2. 2. MOTIVATION TO CHOOSE THIS PAPER Terry Taewoong Um (terry.t.um@gmail.com) • I have applied convolutional neural networks (CNNs) to classify wearable sensor data in my research, but haven’t applied recurrent neural networks (RNNs) in my research. Exercise Motion Classification from Large- Scale Wearable Sensor Data Using CNNs (2016) Classified 50 gym exercises with 92% Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring using CNNs (2017) classification accuracy 77%  92%
  3. 3. 2 baseline papers
  4. 4. MOTION FORECASTING • Motion forecasting (Motion prediction) : Given a person’s past motions, forecast the most likely future 3D poses Terry Taewoong Um (terry.t.um@gmail.com) • e.g.) Sentence completion motion forecasting ≈ a high-dimensional and nonlinear version of sentence completion
  5. 5. BACKGROUND: RNN Terry Taewoong Um (terry.t.um@gmail.com) • Recurrent Neural Networks (RNNs) (unfold)  vanishing or exploding gradient problem  solve by using gate units (Xavier Giro, https://www.slideshare.net/xavigiro/recurrent-neural-networks-1-d2l2-deep-learning-for-speech-and-language-upc-2017)
  6. 6. BACKGROUND: LSTM & GRU Terry Taewoong Um (terry.t.um@gmail.com) • Solution : Let nodes to decide whether forget or bypass the information  Gate units: Long short-term memory(LSTM) or gated recurrent unit (GRU) (Christopher Olah, http://colah.github.io/posts/2015-08-Understanding-LSTMs/) LSTM GRU similar performance, but less computation
  7. 7. BACKGROUND: RNN Terry Taewoong Um (terry.t.um@gmail.com) (Andrej Karpathy, http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
  8. 8. SEQUENCE GENERATION (Andrej Karpathy, http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
  9. 9. SEQUENCE GENERATION (Andrej Karpathy, http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
  10. 10. SIMPLEST APPROACH Terry Taewoong Um (terry.t.um@gmail.com) • Just apply a LSTM to joint angle data (2015) ERD Encoder-Recurrent-Decoder LSTM https://www.youtube.com/wat ch?v=CvaKD1NGcBk [Result] Contribution : It’s the first LSTM work with skeleton data
  11. 11. RELATED WORK ERD Terry Taewoong Um (terry.t.um@gmail.com)
  12. 12. RELATED WORK https://youtu.be/JTr_wkPN-xs?t=1m18s SRNN Terry Taewoong Um (terry.t.um@gmail.com)
  13. 13. C.F.) HIERARCHICAL RNN MHAD dataset (11 actions) HDM05 dataset (65 actions) MSR-Action3D dataset (20 actions) (2016)
  14. 14. MOTION FORECASTING USING RNN Terry Taewoong Um (terry.t.um@gmail.com) • Evaluation criteria • Problem of RNN-based methods for short-term (<=0.5s) for long-term (>=1s) Learning Human Motion Models for Long-term Predictions (2017), P. Ghosh et al.
  15. 15. WHAT’S THE PROBLEMS? Other problems: - Model is so complicated that large data is needed - Action-specific network : use a certain-action data Terry Taewoong Um (terry.t.um@gmail.com)
  16. 16. PROPOSED SOLUTION
  17. 17. PROPOSED SOLUTION (from K. He’s ResNet tutorial at ICML2016)
  18. 18. EXPERIMENT SETTING Terry Taewoong Um (terry.t.um@gmail.com) • Details
  19. 19. RESULTS (SA : single action data, MA: multiple action data)
  20. 20. RESULTS • Zero-velocity shows a good performance • Sampling-based loss gives plausible motion generation + no noise scheduling is needed • Residual connection improves the performance • Using single action data < Using all action data (data quality < data quantity) • Aperiodic motions are hard to model with RNNs • Action labels helps the learning process • Small loss != good qualitative long-term motion  need to propose a new loss • Unsupervised approach gives a comparative result • This research area hasn’t been matured, so, we have a chance . Terry Taewoong Um (terry.t.um@gmail.com) Idea: (for t+1 prediction) Rather than residual input 𝑋𝑡 residual input 𝑋𝑡 + 𝑋𝑡 𝑑𝑡, or explicitly exploiting 𝑋 and 𝑋
  21. 21. BONUS: MORE RESEARCHES FROM 2017 Terry Taewoong Um (terry.t.um@gmail.com)
  22. 22. BONUS: MORE RESEARCHES FROM 2017 Terry Taewoong Um (terry.t.um@gmail.com)
  23. 23. BONUS: MORE RESEARCHES FROM 2017 Terry Taewoong Um (terry.t.um@gmail.com) https://sites.google.com/a/umich.edu/rub enevillegas/hierch_vid https://twitter.com/TerryUm_ML

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