diff --git a/docs/en/dataset_zoo/2d_face_keypoint.md b/docs/en/dataset_zoo/2d_face_keypoint.md
index 62f66bd82b..f0861bd2c3 100644
--- a/docs/en/dataset_zoo/2d_face_keypoint.md
+++ b/docs/en/dataset_zoo/2d_face_keypoint.md
@@ -152,6 +152,8 @@ mmpose
             ...
 ```
 
+We also supply a script which can convert the raw WFLW annotations to COCO style. The script's output is not completely consistent with the annotations downloaded from the URL, but it does not affect training and testing.
+
 ## AFLW Dataset
 
 <!-- [DATASET] -->
diff --git a/docs/zh_cn/dataset_zoo/2d_face_keypoint.md b/docs/zh_cn/dataset_zoo/2d_face_keypoint.md
index 62f66bd82b..f0861bd2c3 100644
--- a/docs/zh_cn/dataset_zoo/2d_face_keypoint.md
+++ b/docs/zh_cn/dataset_zoo/2d_face_keypoint.md
@@ -152,6 +152,8 @@ mmpose
             ...
 ```
 
+We also supply a script which can convert the raw WFLW annotations to COCO style. The script's output is not completely consistent with the annotations downloaded from the URL, but it does not affect training and testing.
+
 ## AFLW Dataset
 
 <!-- [DATASET] -->
diff --git a/tools/dataset_converters/wflw2coco.py b/tools/dataset_converters/wflw2coco.py
new file mode 100644
index 0000000000..c29e3c49ee
--- /dev/null
+++ b/tools/dataset_converters/wflw2coco.py
@@ -0,0 +1,118 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import json
+import math
+import os
+import os.path as osp
+
+import cv2
+import numpy as np
+
+
+def default_dump(obj):
+    """Convert numpy classes to JSON serializable objects."""
+    if isinstance(obj, (np.integer, np.floating, np.bool_)):
+        return obj.item()
+    elif isinstance(obj, np.ndarray):
+        return obj.tolist()
+    else:
+        return obj
+
+
+def convert_wflw_to_coco(ann_file, out_file):
+    annotations = []
+    images = []
+    files = []
+    cnt = 0
+    image_cnt = 0
+
+    data_infos = open(ann_file).readlines()
+    data_infos = [x.strip().split() for x in data_infos]
+    for data in data_infos:
+        file_name = data[-1]
+        img_path = osp.join('data/wflw/WFLW_images', file_name)
+        img = cv2.imread(img_path)
+
+        keypoints = []
+
+        coordinates = [data[i:i + 2] for i in range(0, 196, 2)]
+
+        for coordinate in coordinates:
+            x, y = coordinate[0], coordinate[1]
+            x, y = float(x), float(y)
+            keypoints.append([x, y, 1])
+        keypoints = np.array(keypoints)
+
+        x1, y1, _ = np.amin(keypoints, axis=0)
+        x2, y2, _ = np.amax(keypoints, axis=0)
+        w, h = x2 - x1, y2 - y1
+        scale = math.ceil(max(w, h)) / 200
+        w_new = w / scale
+        h_new = h / scale
+        center = [(x1 + x2) / 2, (y1 + y2) / 2]
+        x1_new = center[0] - w_new / 2
+        y1_new = center[1] - h_new / 2
+        bbox = [x1_new, y1_new, w_new, h_new]
+
+        image = {}
+        # check if the image already exists
+        if file_name in files:
+            image = images[files.index(file_name)]
+        else:
+            image['id'] = image_cnt
+            image['file_name'] = f'{file_name}'
+            image['height'] = img.shape[0]
+            image['width'] = img.shape[1]
+            image_cnt = image_cnt + 1
+            files.append(file_name)
+            images.append(image)
+
+        ann = {}
+        ann['keypoints'] = keypoints.reshape(-1).tolist()
+        ann['image_id'] = image['id']
+        ann['id'] = cnt
+        ann['num_keypoints'] = len(keypoints)
+        ann['bbox'] = bbox
+        ann['is_crowd'] = 0
+        ann['area'] = w * h
+        ann['category_id'] = 1
+        ann['center'] = center
+        ann['scale'] = scale
+        annotations.append(ann)
+        cnt = cnt + 1
+
+    cocotype = {}
+    cocotype['images'] = images
+    cocotype['annotations'] = annotations
+    cocotype['categories'] = [{
+        'supercategory': 'person',
+        'id': 1,
+        'name': 'face',
+        'keypoints': [],
+        'skeleton': []
+    }]
+
+    json.dump(
+        cocotype,
+        open(out_file, 'w'),
+        ensure_ascii=False,
+        default=default_dump)
+    print(f'done {out_file}')
+
+
+if __name__ == '__main__':
+    if not osp.exists('data/wflw/annotations'):
+        os.makedirs('data/wflw/annotations')
+    root_folder = 'data/wflw'
+    ann_folder = f'{root_folder}/WFLW_annotations'
+    for root, dirs, files in os.walk(ann_folder):
+        for file in files:
+            if not file.endswith('txt'):
+                continue
+            print(f'Processing {file}')
+            sub_class = file.split('_')[-1].replace('.txt', '')
+            if sub_class != 'train' and sub_class != 'test':
+                out_file = f'face_landmarks_wflw_test_{sub_class}.json'
+            else:
+                out_file = f'face_landmarks_wflw_{sub_class}.json'
+            convert_wflw_to_coco(f'{root}/{file}',
+                                 f'{root_folder}/annotations/{out_file}')