diff --git a/apps/test_triangle.py b/apps/test_triangle.py
new file mode 100644
index 0000000..dfc7eed
--- /dev/null
+++ b/apps/test_triangle.py
@@ -0,0 +1,142 @@
+import pydiffvg
+import torch
+import skimage
+import numpy as np
+
+# Use GPU if available
+pydiffvg.set_use_gpu(torch.cuda.is_available())
+
+canvas_width, canvas_height = 256, 256
+num_control_points = torch.tensor([2, 2, 2])
+points = torch.tensor([[20.0,  30.0], # base
+                       [50.0,  60.0], # control point
+                       [ 90.0, 198.0], # control point
+                       [ 60.0, 218.0], # base
+                       [ 90.0, 180.0], # control point
+                       [200.0,  85.0], # control point
+                       [230.0,  90.0], # base
+                       [220.0,  70.0], # control point
+                       [130.0,  55.0]]) # control point
+path = pydiffvg.Path(num_control_points = num_control_points,
+                     points = points,
+                     is_closed = True)
+shapes = [path]
+path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]),
+                                 fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0]))
+shape_groups = [path_group]
+scene_args = pydiffvg.RenderFunction.serialize_scene(\
+    canvas_width, canvas_height, shapes, shape_groups)
+
+render = pydiffvg.RenderFunction.apply
+img = render(256, # width
+             256, # height
+             2,   # num_samples_x
+             2,   # num_samples_y
+             0,   # seed
+             None,
+             *scene_args)
+# The output image is in linear RGB space. Do Gamma correction before saving the image.
+pydiffvg.imwrite(img.cpu(), 'results/test_curve/target.png', gamma=2.2)
+target = img.clone()
+
+# Load the obj file, get the vertices/control points
+obj = "imgs/Triangle.obj"
+vertices_tmp, faces_tmp = pydiffvg.obj_to_scene(obj)
+print(float(vertices_tmp[1][1]))
+print(faces_tmp)
+
+vertices = []
+faces = []
+for v in vertices_tmp:
+    vertices.append([float(v[1]), float(v[2])])
+for f in faces_tmp:
+    vs_count = len(f)
+    tmp = []
+    for v in f[1:]:
+        tmp.append(int(v))
+    faces.append(tmp)
+print(vertices)
+print(faces)
+
+# Ternary subdivision
+# Move the path to produce initial guess
+# normalize points for easier learning rate
+points_n = torch.tensor([[vertices[0][0]/256.0, vertices[0][1]/256.0], # base
+                         [70.0/256.0,  140.0/256.0], # control point
+                         [50.0/256.0, 100.0/256.0], # control point
+                         [vertices[1][0]/256.0, vertices[1][1]/256.0], # base
+                         [80.0/256.0, 40.0/256.0], # control point
+                         [120.0/256.0,  40.0/256.0], # control point
+                         [vertices[2][0]/256.0, vertices[2][1]/256.0], # base
+                         [150.0/256.0,  100.0/256.0], # control point
+                         [130.0/256.0,  140.0/256.0]], # control point
+                        requires_grad = True) 
+color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True)
+path.points = points_n * 256
+path_group.fill_color = color
+scene_args = pydiffvg.RenderFunction.serialize_scene(\
+    canvas_width, canvas_height, shapes, shape_groups)
+img = render(256, # width
+             256, # height
+             2,   # num_samples_x
+             2,   # num_samples_y
+             1,   # seed
+             None,
+             *scene_args)
+pydiffvg.imwrite(img.cpu(), 'results/test_curve/init.png', gamma=2.2)
+
+# Optimize
+optimizer = torch.optim.Adam([points_n, color], lr=1e-2)
+# Run 100 Adam iterations.
+for t in range(100):
+    print('iteration:', t)
+    optimizer.zero_grad()
+    # Forward pass: render the image.
+    path.points = points_n * 256
+    path_group.fill_color = color
+    scene_args = pydiffvg.RenderFunction.serialize_scene(\
+        canvas_width, canvas_height, shapes, shape_groups)
+    img = render(256,   # width
+                 256,   # height
+                 2,     # num_samples_x
+                 2,     # num_samples_y
+                 t+1,   # seed
+                 None,
+                 *scene_args)
+    # Save the intermediate render.
+    pydiffvg.imwrite(img.cpu(), 'results/test_curve/iter_{}.png'.format(t), gamma=2.2)
+    # Compute the loss function. Here it is L2.
+    loss = (img - target).pow(2).sum()
+    print('loss:', loss.item())
+
+    # Backpropagate the gradients.
+    loss.backward()
+    # Print the gradients
+    print('points_n.grad:', points_n.grad)
+    print('color.grad:', color.grad)
+
+    # Take a gradient descent step.
+    optimizer.step()
+    # Print the current params.
+    print('points:', path.points)
+    print('color:', path_group.fill_color)
+
+# Render the final result.
+scene_args = pydiffvg.RenderFunction.serialize_scene(\
+    canvas_width, canvas_height, shapes, shape_groups)
+img = render(256,   # width
+             256,   # height
+             2,     # num_samples_x
+             2,     # num_samples_y
+             102,    # seed
+             None,
+             *scene_args)
+# Save the images and differences.
+pydiffvg.imwrite(img.cpu(), 'results/test_curve/final.png')
+
+# Convert the intermediate renderings to a video.
+from subprocess import call
+call(["ffmpeg", "-framerate", "24", "-i",
+    "results/test_curve/iter_%d.png", "-vb", "20M",
+    "results/test_curve/out.mp4"])
+
diff --git a/apps/test_triangles.py b/apps/test_triangles.py
new file mode 100644
index 0000000..c3960e7
--- /dev/null
+++ b/apps/test_triangles.py
@@ -0,0 +1,144 @@
+import pydiffvg
+import torch
+import skimage
+import numpy as np
+
+# Use GPU if available
+pydiffvg.set_use_gpu(torch.cuda.is_available())
+
+canvas_width, canvas_height = 256, 256
+num_control_points = torch.tensor([2, 2, 2])
+points = torch.tensor([[20.0,  30.0], # base
+                       [50.0,  60.0], # control point
+                       [ 90.0, 198.0], # control point
+                       [ 60.0, 218.0], # base
+                       [ 90.0, 180.0], # control point
+                       [200.0,  85.0], # control point
+                       [230.0,  90.0], # base
+                       [220.0,  70.0], # control point
+                       [130.0,  55.0]]) # control point
+path = pydiffvg.Path(num_control_points = num_control_points,
+                     points = points,
+                     is_closed = True)
+shapes = [path]
+path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]),
+                                 fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0]))
+shape_groups = [path_group]
+scene_args = pydiffvg.RenderFunction.serialize_scene(\
+    canvas_width, canvas_height, shapes, shape_groups)
+
+render = pydiffvg.RenderFunction.apply
+img = render(256, # width
+             256, # height
+             2,   # num_samples_x
+             2,   # num_samples_y
+             0,   # seed
+             None,
+             *scene_args)
+# The output image is in linear RGB space. Do Gamma correction before saving the image.
+pydiffvg.imwrite(img.cpu(), 'results/test_triangles/target.png', gamma=2.2)
+target = img.clone()
+
+# Load the obj file, get the vertices/control points
+obj = "imgs/Triangles.obj"
+vertices_tmp, faces_tmp = pydiffvg.obj_to_scene(obj)
+print(float(vertices_tmp[1][1]))
+print(faces_tmp)
+
+vertices = []
+faces = []
+for v in vertices_tmp:
+    vertices.append([float(v[1]), float(v[2])])
+for f in faces_tmp:
+    vs_count = len(f)
+    tmp = []
+    for v in f[1:]:
+        tmp.append(int(v))
+    faces.append(tmp)
+print(vertices)
+print(faces)
+
+quit()
+
+# Ternary subdivision
+# Move the path to produce initial guess
+# normalize points for easier learning rate
+points_n = torch.tensor([[vertices[0][0]/256.0, vertices[0][1]/256.0], # base
+                         [70.0/256.0,  140.0/256.0], # control point
+                         [50.0/256.0, 100.0/256.0], # control point
+                         [vertices[1][0]/256.0, vertices[1][1]/256.0], # base
+                         [80.0/256.0, 40.0/256.0], # control point
+                         [120.0/256.0,  40.0/256.0], # control point
+                         [vertices[2][0]/256.0, vertices[2][1]/256.0], # base
+                         [150.0/256.0,  100.0/256.0], # control point
+                         [130.0/256.0,  140.0/256.0]], # control point
+                        requires_grad = True) 
+color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True)
+path.points = points_n * 256
+path_group.fill_color = color
+scene_args = pydiffvg.RenderFunction.serialize_scene(\
+    canvas_width, canvas_height, shapes, shape_groups)
+img = render(256, # width
+             256, # height
+             2,   # num_samples_x
+             2,   # num_samples_y
+             1,   # seed
+             None,
+             *scene_args)
+pydiffvg.imwrite(img.cpu(), 'results/test_triangles/init.png', gamma=2.2)
+
+# Optimize
+optimizer = torch.optim.Adam([points_n, color], lr=1e-2)
+# Run 100 Adam iterations.
+for t in range(100):
+    print('iteration:', t)
+    optimizer.zero_grad()
+    # Forward pass: render the image.
+    path.points = points_n * 256
+    path_group.fill_color = color
+    scene_args = pydiffvg.RenderFunction.serialize_scene(\
+        canvas_width, canvas_height, shapes, shape_groups)
+    img = render(256,   # width
+                 256,   # height
+                 2,     # num_samples_x
+                 2,     # num_samples_y
+                 t+1,   # seed
+                 None,
+                 *scene_args)
+    # Save the intermediate render.
+    pydiffvg.imwrite(img.cpu(), 'results/test_triangles/iter_{}.png'.format(t), gamma=2.2)
+    # Compute the loss function. Here it is L2.
+    loss = (img - target).pow(2).sum()
+    print('loss:', loss.item())
+
+    # Backpropagate the gradients.
+    loss.backward()
+    # Print the gradients
+    print('points_n.grad:', points_n.grad)
+    print('color.grad:', color.grad)
+
+    # Take a gradient descent step.
+    optimizer.step()
+    # Print the current params.
+    print('points:', path.points)
+    print('color:', path_group.fill_color)
+
+# Render the final result.
+scene_args = pydiffvg.RenderFunction.serialize_scene(\
+    canvas_width, canvas_height, shapes, shape_groups)
+img = render(256,   # width
+             256,   # height
+             2,     # num_samples_x
+             2,     # num_samples_y
+             102,    # seed
+             None,
+             *scene_args)
+# Save the images and differences.
+pydiffvg.imwrite(img.cpu(), 'results/test_triangles/final.png')
+
+# Convert the intermediate renderings to a video.
+from subprocess import call
+call(["ffmpeg", "-framerate", "24", "-i",
+    "results/test_triangles/iter_%d.png", "-vb", "20M",
+    "results/test_triangles/out.mp4"])
+