diff --git a/render_shapenet_data/make_data_split.py b/render_shapenet_data/make_data_split.py
new file mode 100644
index 0000000..72abea4
--- /dev/null
+++ b/render_shapenet_data/make_data_split.py
@@ -0,0 +1,49 @@
+# This script generates data manifests for your dataset, divided into training, testing and evaluation lists
+
+# args:
+# sys.argv[1] = name or id of the synset
+# sys.argv[2] = directory of the synset to generate from (should be a list of directories)
+# sys.argv[3] = output directory (optional, defaults to "./3dgan_data_split")
+
+import os
+import sys
+import random
+
+# the second arg is the source directory
+source_dir = sys.argv[2]
+
+# the first arg is the name, if it is null then set it to the last part of the source directory
+name = sys.argv[1] if len(sys.argv) > 2 else source_dir.split("/")[-1]
+
+# default output directory is 3dgan_data_split, but you can specify a different one
+data_dir = "3dgan_data_split"
+
+if len(sys.argv) > 3:
+    data_dir = sys.argv[3]
+
+os.makedirs(data_dir, exist_ok=True)
+
+os.makedirs(os.path.join(data_dir, name), exist_ok=True)
+
+all = []
+
+for folder in os.listdir(source_dir):
+    all.append(folder)
+
+train = [] # 70%
+test = [] # 20%
+val = [] # 10%
+
+random.shuffle(all)
+train = all[:int(len(all) * 0.7)]
+test = all[int(len(all) * 0.7):int(len(all) * 0.9)]
+val = all[int(len(all) * 0.9):]
+
+with open(os.path.join(data_dir, name, "train.txt"), "w") as f:
+    f.write("\n".join(train))
+with open(os.path.join(data_dir, name, "test.txt"), "w") as f:
+    f.write("\n".join(test))
+with open(os.path.join(data_dir, name, "val.txt"), "w") as f:
+    f.write("\n".join(val))
+
+print ("Finished");
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