be compatible with new versions of open3d

README.md

@@ -20,7 +20,9 @@ Some links:
 [\[supp doc\]](https://calciferzh.github.io/files/zhou2020monocular_supp.pdf)
 [\[webpage\]](https://calciferzh.github.io/publications/zhou2020monocular)
 
-The release of training code is still in progress.
+The author is too busy to collect the training code for release.
+On the other hand, it should not be difficult to implement the training part.
+Feel free to open an issue for any encountered problems.
 
 ## Usage
 
@@ -41,7 +43,7 @@ Please check `requirements.txt`. All dependencies are available via pip and cond
 1. `python app.py`
 1. Put your **right hand** in front of the camera. The pre-trained model is for left hand, but the input would be flipped internally.
 1. Press `ESC` to quit.
-1. Although the model is robust to variant scales, most ideally the image should be 1.3x larger than the hand boudning box. A good bounding box may result in better accuracy. You can track the bounding box with the 2D predictions of the model.
+1. Although the model is robust to variant scales, most ideally the image should be 1.3x larger than the hand bounding box. A good bounding box may result in better accuracy. You can track the bounding box with the 2D predictions of the model.
 
 We found that the model may fail on some "simple" poses. We think this is because such poses were no presented in the training data. We are working on a v2 version with further extended data to tackle this problem.
 

app.py

@@ -97,8 +97,7 @@ def live_application(capture):
     mesh.paint_uniform_color(config.HAND_COLOR)
     mesh.compute_triangle_normals()
     mesh.compute_vertex_normals()
-    # for some version of open3d you may need `viewer.update_geometry(mesh)`
-    viewer.update_geometry()
+    viewer.update_geometry(mesh)
 
     viewer.poll_events()