add demo code

Vegetebird · Jun 27, 2022 · 5fbb024 · 5fbb024
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diff --git a/README.md b/README.md
@@ -11,7 +11,7 @@ This is the official implementation of the approach described in the paper:
 
 
 ## News
--  03/24/2022: Demo and in-the-wild inference code is released!
+-  03/24/2022: Demo and in-the-wild inference code are released!
 - 03/15/2022: Our method has been verified in [self-supervised pre-training](https://arxiv.org/pdf/2203.07628.pdf) as a backbone network! 
 
 
@@ -78,7 +78,7 @@ Sample demo output:
 If you find our work useful in your research, please consider citing:
 
     @article{li2022exploiting,
-      title={Exploiting temporal contexts with strided transformer for 3d human pose estimation},
+      title={Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation},
       author={Li, Wenhao and Liu, Hong and Ding, Runwei and Liu, Mengyuan and Wang, Pichao and Yang, Wenming},
       journal={IEEE Transactions on Multimedia},
       year={2022},

diff --git a/main.py b/main.py
@@ -156,7 +156,7 @@ def input_augmentation(input_2D, model_trans):
     if opt.reload:
         model_path = sorted(glob.glob(os.path.join(opt.previous_dir, '*.pth')))
 
-        refine_path = []
+        no_refine_path = []
         for path in model_path:
             if path.split('/')[-1][0] == 'n':
                 no_refine_path = path