diff --git a/README.md b/README.md
index f939a29..1ce7351 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,14 @@
-# Monoloco library     [![Downloads](https://pepy.tech/badge/monoloco)](https://pepy.tech/project/monoloco)
+# Monoloco library       [![Downloads](https://pepy.tech/badge/monoloco)](https://pepy.tech/project/monoloco)
 
 <img src="docs/monoloco.gif" alt="gif" />
 
 
-This library is based on three research projects for monocular/stereo 3D human localization (detection), body orientation, and social distancing.
+This library is based on three research projects for monocular/stereo 3D human localization (detection), body orientation, and social distancing. Check the [demo video](https://www.youtube.com/watch?v=O5zhzi8mwJ4)!
 
 > __MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization__<br /> 
 > _[L. Bertoni](https://scholar.google.com/citations?user=f-4YHeMAAAAJ&hl=en), [S. Kreiss](https://www.svenkreiss.com), 
 [T. Mordan](https://people.epfl.ch/taylor.mordan/?lang=en), [A. Alahi](https://scholar.google.com/citations?user=UIhXQ64AAAAJ&hl=en)_, ICRA 2021 <br /> 
-__[Article](https://arxiv.org/abs/2008.10913)__  &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;   __[Citation](#Citation)__  &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; __[Video](#Todo)__
+__[Article](https://arxiv.org/abs/2008.10913)__  &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;   __[Citation](#Citation)__  &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; __[Video](https://www.youtube.com/watch?v=pGssROjckHU)__
      
 <img src="docs/out_000840_multi.jpg" width="700"/>
 
@@ -188,7 +188,7 @@ The network estimates orientation and box dimensions as well. Results are saved
 <br /> 
 
 ## Training
-We train on the KITTI dataset (MonoLoco/Monoloco++/MonStereo) or the nuScenes dataset (MonoLoco) specifying the path of the json file containing the input joints. Please download them [heere](https://drive.google.com/file/d/1e-wXTO460ip_Je2NdXojxrOrJ-Oirlgh/view?usp=sharing) or follow [preprocessing instructions](#Preprocessing).
+We train on the KITTI dataset (MonoLoco/Monoloco++/MonStereo) or the nuScenes dataset (MonoLoco) specifying the path of the json file containing the input joints. Please download them [here](https://drive.google.com/file/d/1bJPyA1HuX9uyJYf1IhiDqzhkvSokd4l0/view?usp=sharing) or follow [preprocessing instructions](#Preprocessing).
 
 Results for MonoLoco++ are obtained with: 
 
@@ -321,8 +321,6 @@ python -m monoloco.run eval \
 --save \
 ````
 
-<img src="docs/results_monoloco_pp." width="550"/>
-By changing the net and the model, the same command evaluates MonStereo model.
 
 <img src="docs/results_stereo.jpg" width="550"/>
 
@@ -355,7 +353,7 @@ When using this library in your research, we will be happy if you cite us!
 @InProceedings{bertoni_2021_icra,
     author = {Bertoni, Lorenzo and Kreiss, Sven and Mordan, Taylor and Alahi, Alexandre},
     title = {MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization},
-    booktitle = {International Conference on Robotics and Automation (ICRA)},
+    booktitle = {the International Conference on Robotics and Automation (ICRA)},
     year = {2021}
 }
 ```
@@ -372,7 +370,7 @@ When using this library in your research, we will be happy if you cite us!
 @InProceedings{bertoni_2019_iccv,
     author = {Bertoni, Lorenzo and Kreiss, Sven and Alahi, Alexandre},
     title = {MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation},
-    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
+    booktitle = {the IEEE International Conference on Computer Vision (ICCV)},
     month = {October},
     year = {2019}
 }