diff --git a/NLFPoseExtract/nlf_render.py b/NLFPoseExtract/nlf_render.py
index 9abb43c..b0fe7e6 100644
--- a/NLFPoseExtract/nlf_render.py
+++ b/NLFPoseExtract/nlf_render.py
@@ -146,7 +146,7 @@ def collect_smpl_poses_samurai(data):
 
 
 
-def render_nlf_as_images(smpl_poses, dw_poses, height, width, video_length, intrinsic_matrix=None, draw_2d=True):
+def render_nlf_as_images(smpl_poses, dw_poses, height, width, video_length, intrinsic_matrix=None, draw_2d=True, draw_face=True, draw_hands=True):
     """ return a list of images """
 
     base_colors_255_dict = {
@@ -244,7 +244,7 @@ def render_nlf_as_images(smpl_poses, dw_poses, height, width, video_length, intr
 
     frames_np_rgba = render_whole(cylinder_specs_list, H=height, W=width, fx=focal_x, fy=focal_y, cx=princpt[0], cy=princpt[1])
     if dw_poses is not None and draw_2d:
-        canvas_2d = draw_pose_to_canvas_np(aligned_poses, pool=None, H=height, W=width, reshape_scale=0, show_feet_flag=False, show_body_flag=False, show_cheek_flag=True, dw_hand=True)
+        canvas_2d = draw_pose_to_canvas_np(aligned_poses, pool=None, H=height, W=width, reshape_scale=0, show_feet_flag=False, show_body_flag=False, show_cheek_flag=True, dw_hand=True, show_face_flag=draw_face, show_hand_flag=draw_hands)
 
         for i in range(len(frames_np_rgba)):
             frame_img = frames_np_rgba[i]
diff --git a/nodes.py b/nodes.py
index a8b2078..4aa2cb0 100644
--- a/nodes.py
+++ b/nodes.py
@@ -164,6 +164,8 @@ class RenderNLFPoses:
             "optional": {
                 "dw_poses": ("DWPOSES", {"default": None, "tooltip": "Optional DW pose model for 2D drawing"}),
                 "ref_dw_pose": ("DWPOSES", {"default": None, "tooltip": "Optional reference DW pose model for alignment"}),
+                "draw_face": ("BOOLEAN", {"default": True, "tooltip": "Whether to draw face keypoints"}),
+                "draw_hands": ("BOOLEAN", {"default": True, "tooltip": "Whether to draw hand keypoints"}),
             }
     }
 
@@ -172,7 +174,7 @@ class RenderNLFPoses:
     FUNCTION = "predict"
     CATEGORY = "WanVideoWrapper"
 
-    def predict(self, nlf_poses, width, height, dw_poses=None, ref_dw_pose=None):
+    def predict(self, nlf_poses, width, height, dw_poses=None, ref_dw_pose=None, draw_face=True, draw_hands=True):
 
         from .NLFPoseExtract.nlf_render import render_nlf_as_images, shift_dwpose_according_to_nlf, process_data_to_COCO_format, intrinsic_matrix_from_field_of_view
         from .NLFPoseExtract.align3d import solve_new_camera_params_central, solve_new_camera_params_down
@@ -224,9 +226,11 @@ class RenderNLFPoses:
             logging.info(f"Scale - m: {scale_m}, face: {scale_face}")
             shift_dwpose_according_to_nlf(pose_input, dw_pose_input, ori_camera_pose, new_camera_intrinsics, height, width)
 
-            frames_np = render_nlf_as_images(pose_input, dw_pose_input, height, width, len(pose_input), intrinsic_matrix=new_camera_intrinsics)
+            intrinsic_matrix = new_camera_intrinsics
         else:
-            frames_np = render_nlf_as_images(pose_input, dw_pose_input, height, width, len(pose_input), intrinsic_matrix=ori_camera_pose)
+            intrinsic_matrix = ori_camera_pose
+
+        frames_np = render_nlf_as_images(pose_input, dw_pose_input, height, width, len(pose_input), intrinsic_matrix=intrinsic_matrix, draw_face=draw_face, draw_hands=draw_hands)
 
         frames_tensor = torch.from_numpy(np.stack(frames_np, axis=0)).contiguous() / 255.0
         frames_tensor, mask = frames_tensor[..., :3], frames_tensor[..., -1] > 0.5