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Updated README
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-## Learning Trajectory Dependencies for Human Motion Prediction
+## Generative Model-Enhanced Human Motion Prediction
 This is the code for the paper
 
-Wei Mao, Miaomiao Liu, Mathieu Salzmann, Hongdong Li. 
-[_Learning Trajectory Dependencies for Human Motion Prediction_](https://arxiv.org/abs/1908.05436). In ICCV 19.
+Anthony Bourached, Ryan-Rhys Griffiths, Robert Gray, Parashkev Nachev. 
+[_Generative Model-Enhanced Human Motion Prediction_]
 
 ### Dependencies
 
@@ -16,117 +16,9 @@ Wei Mao, Miaomiao Liu, Mathieu Salzmann, Hongdong Li.
 
 [CMU mocap](http://mocap.cs.cmu.edu/) was obtained from the [repo](https://github.com/chaneyddtt/Convolutional-Sequence-to-Sequence-Model-for-Human-Dynamics) of ConvSeq2Seq paper.
 
-[3DPW](https://virtualhumans.mpi-inf.mpg.de/3DPW/) from their official website.
-
-### Quick demo and visualization
-
-For a quick demo, you can train for a few epochs and visualize the outputs
-of your model.
-
-To train, run
-```bash
-python main.py --epoch 5 --input_n 10 --output 10 --dct_n 20 --data_dir [Path To Your H36M data]/h3.6m/dataset/
-```
-
-Visualize the results of pretrained model for predictions on angle space on H36M dataset.
-* change the model path
-* then run the command below
-```bash
-python demo.py --input_n 10 --output_n 10 --dct_n 20 --data_dir [Path To Your H36M data]/h3.6m/dataset/
-```
-### Training commands
-All the running args are defined in [opt.py](utils/opt.py). We use following commands to train on different datasets and representations.
-To train on angle space,
-```bash
-python main.py --data_dir "[Path To Your H36M data]/h3.6m/dataset/" --input_n 10 --output_n 10 --dct_n 20 --exp [where to save the log file]
-```
-```bash
-python main_cmu.py --data_dir_cmu "[Path To Your CMU data]/cmu_mocap/" --input_n 10 --output_n 25 --dct_n 35 --exp [where to save the log file]
-```
-```bash
-python main_3dpw.py --data_dir_3dpw "[Path To Your 3DPW data]/3DPW/sequenceFiles/" --input_n 10 --output_n 30 --dct_n 40 --exp [where to save the log file]
-```
-To train on 3D space,
-```bash
-python3 main_3d.py --data_dir "[Path To Your H36M data]/h3.6m/dataset/" --input_n 10 --output_n 10 --dct_n 15 --exp [where to save the log file]
-```
-```bash
-python main_cmu_3d.py --data_dir_cmu "[Path To Your CMU data]/cmu_mocap/" --input_n 10 --output_n 25 --dct_n 30 --exp [where to save the log file]
-```
-```bash
-python main_3dpw_3d.py --data_dir_3dpw "[Path To Your 3DPW data]/3DPW/sequenceFiles/" --input_n 10 --output_n 30 --dct_n 35 --exp [where to save the log file]
-```
-
-
-### Results
-We re-run our code 2 more times under different setups and the overall average results at different time are reported below.
-
-* Human3.6-short-term prediction on angle space (top) and 3D coordinate (bottom)
-
-|                | 80ms   | 160ms  | 320ms  | 400ms  |
-|----------------|------|------|------|------|
-| pre-trained | 0.27 | 0.51 | 0.83 | 0.95 |
-| test_run_1     | 0.28 | 0.52 | 0.84 | 0.96 |
-| test_run_2     | 0.28 | 0.52 | 0.84 | 0.96 |
-|----------------|------|------|------|------|
-| pre-trained | 12.1 | 25.0 | 51.0 | 61.3 |
-| test_run_1 | 12.1 | 24.6 | 50.4 | 61.1 |
-| test_run_2 | 12.1 | 24.8 | 50.5 | 61.2 |
-
-* Human3.6-long-term prediction
-
-|             | 560ms  |1000ms|
-|-------------|--------|------|
-| pre-trained | 0.90   | 1.27 |
-| test_run_1  | 0.91   | 1.25 |
-| test_run_2  | 0.92   | 1.27 |
-|-------------|--------|------|
-| pre-trained | 50.4   | 71.0 |
-| test_run_1  | 51.2   | 71.6 |
-| test_run_2  | 51.6   | 70.9 |
-
-
-* CMU-mocap
-
-|             | 80ms | 160ms | 320ms | 400ms | 1000ms |
-|-------------|------|-------|-------|-------|--------|
-| pre-trained | 0.25 | 0.39  | 0.68  | 0.79  | 1.33   |
-| test_run_1  | 0.26 | 0.41  | 0.72  | 0.84  | 1.35   |
-| test_run_2  | 0.26 | 0.41  | 0.71  | 0.83  | 1.38   |
-|-------------|------|-------|-------|-------|--------|
-| pre-trained | 11.5 | 20.4  | 37.8  | 46.8  | 96.5   |
-| test_run_1  | 11.3 | 19.8  | 36.9  | 45.5  | 92.7   |
-| test_run_2  | 11.3 | 19.7  | 37.2  | 46.0  | 94.0   |
-
-* 3DPW
-
-|             | 200ms | 400ms | 600ms | 800ms | 1000ms |
-|-------------|-------|-------|-------|-------|--------|
-| pre-trained | 0.64  | 0.95  | 1.12  | 1.22  | 1.27   |
-| test_run_1  | 0.64  | 0.97  | 1.12  | 1.22  | 1.28   |
-| test_run_2  | 0.64  | 0.95  | 1.11  | 1.21  | 1.27   |
-|-------------|-------|-------|-------|-------|--------|
-| pre-trained | 35.6  | 67.8  | 90.6  | 106.9 | 117.8  |
-| test_run_1  | 36.7  | 69.6  | 90.8  | 105.0 | 115.3  |
-| test_run_2  | 35.8  | 69.1  | 93.2  | 110.9 | 121.7  |
-
-
-### Citing
-
-If you use our code, please cite our work
-
-```
-@inproceedings{wei2019motion,
-  title={Learning Trajectory Dependencies for Human Motion Prediction},
-  author={Wei, Mao and Miaomiao, Liu and Mathieu, Salzemann and Hongdong, Li},
-  booktitle={ICCV},
-  year={2019}
-}
-```
-
 ### Acknowledgments
 
-Some of our evaluation code and data process code was adapted/ported from [Residual Sup. RNN](https://github.com/una-dinosauria/human-motion-prediction) by [Julieta](https://github.com/una-dinosauria). The overall code framework (dataloading, training, testing etc.) is adapted from [3d-pose-baseline](https://github.com/una-dinosauria/3d-pose-baseline). 
+The codebase is built on that of https://github.com/wei-mao-2019/LearnTrajDep
 
 ### Licence
 MIT