diff --git a/README.md b/README.md
index b8fb1f6..b27c91e 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,6 @@
 # Mesh Self Intersection
 
-This package provides a PyTorch module used to detect self-intersections in a
-triangle mesh.
+This package provides a PyTorch module that can efficiently (1) detect and (2) penalize (self-)intersections for a triangular mesh.
 
 
 ## Table of Contents
@@ -34,15 +33,17 @@ agreement will automatically terminate your rights under this
 This repository provides a PyTorch wrapper around a CUDA kernel that implements
 the method described in [Maximizing parallelism in the construction of BVHs,
 octrees, and k-d trees](https://dl.acm.org/citation.cfm?id=2383801). More
-specifically, given input meshes with the *same* number of faces, it builds a
+specifically, given an input mesh it builds a
 BVH tree for each one and queries it for self-intersections. Moreover, we
-provide a conical distance field based loss for resolving the interpenetrations.
+provide a conical 3D distance field based loss for resolving the interpenetrations, as in [Capturing Hands in Action using Discriminative Salient Points and Physics Simulation](https://doi.org/10.1007/s11263-016-0895-4).
+
+Please note that in the current implementation, for batching one needs to provide meshes with the *same* number of faces. Moreover, the code by default works for self-penetrations of a body mesh. The module can be used also for inter-penetrations of different meshes - for this the easiest and naive approach (without additional bookkeeping) is to fuse all meshes in a single mesh and treat inter-penetrations as self-penetrations.
 
 
 ## Installation
 
 Before installing anything please make sure to set the environment variable
-*$CUDA_SAMPLES_INC* to the path that contains the header `helper_math.h`.
+*$CUDA_SAMPLES_INC* to the path that contains the header `helper_math.h` (provided by NVidia SDK samples).
 To install the module you can:
 
 1. Clone this repository and install it using the *setup.py* script:
@@ -69,17 +70,16 @@ install torch-mesh-isect[all]
   ```Shell
   python examples/detect_and_plot_collisions.py PATH_TO_MESH
   ```
-* [Batch Collision resolution](./examples/batch_smpl_untangle.py):  Resolve self	
-  penetrations for a batch of body models. To run use:
+* [Batch Collision resolution](./examples/batch_smpl_untangle.py):  Resolve self-penetrations for a batch of body models. To run use:
   ```Shell
   python examples/batch_smpl_untangle.py --coll_loss_weight=$WEIGHT 
   --param_fn PKL_FN1 PKL_FN2 ... PKL_FNN  
   ```
-  where PKL_FN* are the filenames of the .pkl files that contain the parameters for each body model. 
+  where PKL_FN* are the filenames of the .pkl files that [can be downloaded here](https://owncloud.tuebingen.mpg.de/index.php/s/bEKMdqf5WbN4MnH) and contain the parameters for each body model. 
 
 ## Dependencies
 
-1. [PyTorch](https://pytorch.org/)
+1. [PyTorch](https://pytorch.org)
 
 ### Example dependencies
 
@@ -87,19 +87,30 @@ install torch-mesh-isect[all]
 
 ### Optional Dependencies
 
-1. [Trimesh](https://trimsh.org/) for loading triangular meshes
-2. [Pyrender](https://pyrender.readthedocs.io/) for visualization
+1. [Trimesh](https://trimsh.org) for loading triangular meshes
+2. [Pyrender](https://pyrender.readthedocs.io) for visualization
 
 ## Citation
 
-If you find this code useful in your research then please cite:
+If you find this code useful in your research then please cite the following two works, for detecting and penalizing mesh intersections accordingly:
 
 ```
-@inproceedings{SMPL-X:2019,
-title = {Expressive Body Capture: 3D Hands, Face, and Body from a Single Image},
-author = {Pavlakos, Georgios and Choutas, Vasileios and Ghorbani, Nima and Bolkart, Timo and Osman, Ahmed A. A. and Tzionas, Dimitrios and Black, Michael J.},
-booktitle = {Proceedings IEEE Conf.
-on Computer Vision and Pattern Recognition (CVPR)}, year = {2019} }
+@inproceedings{Karras:2012:MPC:2383795.2383801,
+author = {Karras, Tero},
+title = {Maximizing Parallelism in the Construction of BVHs, Octrees, and K-d Trees},
+booktitle = {Proceedings of the Fourth ACM SIGGRAPH / Eurographics Conference on High-Performance Graphics},
+series = {EGGH-HPG'12},
+year = {2012},
+isbn = {978-3-905674-41-5},
+location = {Paris, France},
+pages = {33--37},
+numpages = {5},
+url = {https://doi.org/10.2312/EGGH/HPG12/033-037}, 
+doi = {10.2312/EGGH/HPG12/033-037},
+acmid = {2383801},
+publisher = {Eurographics Association},
+address = {Goslar Germany, Germany}, 
+}
 ```
 
 ```
@@ -111,28 +122,19 @@ number = {2},
 pages = {172--193},
 month = jun,
 year = {2016},
-url = {https://doi.org/10.1007/s11263-016-0895-4}, month_numeric
-= {6} }
+url = {https://doi.org/10.1007/s11263-016-0895-4}, 
+month_numeric = {6} }
 ```
 
+This repo was originally developed for the following work, you might be interested in having a look:
+
 ```
-@inproceedings{Karras:2012:MPC:2383795.2383801,
-author = {Karras, Tero},
-title = {Maximizing Parallelism in the Construction of BVHs, Octrees, and K-d Trees},
-booktitle = {Proceedings of the Fourth ACM SIGGRAPH / Eurographics
-Conference on High-Performance Graphics},
-series = {EGGH-HPG'12},
-year = {2012},
-isbn = {978-3-905674-41-5},
-location = {Paris, France},
-pages = {33--37},
-numpages = {5},
-url = {https://doi.org/10.2312/EGGH/HPG12/033-037}, doi =
-{10.2312/EGGH/HPG12/033-037},
-acmid = {2383801},
-publisher = {Eurographics
-Association},
-address = {Goslar Germany, Germany}, }
+@inproceedings{SMPL-X:2019,
+title = {Expressive Body Capture: 3D Hands, Face, and Body from a Single Image},
+author = {Pavlakos, Georgios and Choutas, Vasileios and Ghorbani, Nima and Bolkart, Timo and Osman, Ahmed A. A. and Tzionas, Dimitrios and Black, Michael J.},
+booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)}, 
+year = {2019}
+}
 ```
 
 ## Contact