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diff --git a/vis_st4rtrack.py b/vis_st4rtrack.py
index 28a81c48b20f919e48a38bea4be531a5018c984a..d6164420f8119c68af852d2752495d66c1ccd0b1 100644
--- a/vis_st4rtrack.py
+++ b/vis_st4rtrack.py
@@ -33,21 +33,24 @@ def visualize_st4rtrack(
     up_dir: str = "-z", # should be +z or -z
     max_frames: int = 32,
     share: bool = False,
-    point_size: float = 0.0015,
-    downsample_factor: int = 5,
+    point_size: float = 0.001,
+    downsample_factor: int = 3,
     num_traj_points: int = 100,
-    conf_thre: float = 1.,
+    conf_thre_percentile: float = 1,
     traj_end_frame: int = 100,
     traj_start_frame: int = 0,
     traj_line_width: float = 3.,
-    fixed_length_traj: int = 10,
+    fixed_length_traj: int = 15,
     server: viser.ViserServer = None,
     use_float16: bool = True,
     preloaded_data: dict = None,  # Add this parameter to accept preloaded data
-    color_code: str = "rainbow",
-    blue_rgb: tuple[float, float, float] = (0., 0., 1.),
-    red_rgb: tuple[float, float, float] = (1., 0., 0.),
+    color_code: str = "jet",
+    # Updated hex colors: #002676 for blue and #FDB515 for red/gold
+    blue_rgb: tuple[float, float, float] = (0.0, 0.149, 0.463),  # #002676
+    red_rgb: tuple[float, float, float] = (0.769, 0.510, 0.055),   # #FDB515
     blend_ratio: float = 0.7,
+    mask_folder: str = None,
+    mid_anchor: bool = False,
 ) -> None:
     log_memory_usage("at start of visualization")
     
@@ -58,7 +61,7 @@ def visualize_st4rtrack(
 
     @server.on_client_connect
     def _(client: viser.ClientHandle) -> None:
-        client.camera.position = (1e-3, 0.675, -0.1)
+        client.camera.position = (1e-3, 0.75, -0.1)
         client.camera.look_at = (0, 0, 0)
 
     # Configure the GUI panel size and layout
@@ -89,21 +92,15 @@ def visualize_st4rtrack(
         traj_3d_head2 = None
         conf_mask_head1 = None
         conf_mask_head2 = None
-        mask_folder = './train'
-        masks_paths = sorted(glob(mask_folder + '/*.jpg'))
-        
-        # Check if masks exist
-        if masks_paths:
+        masks = None
+        if mask_folder is not None:
+            masks_paths = sorted(glob(mask_folder + '/*.jpg'))
             masks = [iio.imread(p) for p in masks_paths]
             masks = np.stack(masks, axis=0)
             # Convert masks to binary (0 or 1)
             masks = (masks < 1).astype(np.float32)
             masks = masks.sum(axis=-1) > 2  # Combine all channels, True where any channel was 1
             print(f"Original masks shape: {masks.shape}")
-        else:
-            # If no masks found, create default masks (all ones)
-            print("No masks found. Creating default masks (all ones).")
-            masks = None  # Will be initialized with proper dimensions later
         
         if Path(traj_path).is_dir():
             # Load head1 data
@@ -128,10 +125,10 @@ def visualize_st4rtrack(
                 log_memory_usage("after loading head1 data")
                 
                 h, w, _ = traj_3d_head1.shape[1:]
-                
+                num_frames = traj_3d_head1.shape[0]
+
                 # If masks is None, create default masks (all ones)
                 if masks is None:
-                    num_frames = traj_3d_head1.shape[0]
                     masks = np.ones((num_frames, h, w), dtype=bool)
                     print(f"Created default masks with shape: {masks.shape}")
                 else:
@@ -153,6 +150,11 @@ def visualize_st4rtrack(
                 if conf_paths_head1:
                     conf_head1 = onp.stack([onp.load(p).astype(onp.float16) for p in conf_paths_head1], axis=0)
                     conf_head1 = conf_head1.reshape(conf_head1.shape[0], -1)
+                    conf_head1 = conf_head1.mean(axis=0)
+                    # repeat the conf_head1 to match the number of frames in the dimension 0
+                    conf_head1 = np.tile(conf_head1, (num_frames, 1))
+                    # Convert to float32 before calculating percentile to avoid overflow
+                    conf_thre = np.percentile(conf_head1.astype(np.float32), conf_thre_percentile)
                     conf_mask_head1 = conf_head1 > conf_thre
 
             # Process head2
@@ -168,7 +170,9 @@ def visualize_st4rtrack(
                 if conf_paths_head2:
                     conf_head2 = onp.stack([onp.load(p).astype(onp.float16) for p in conf_paths_head2], axis=0)
                     conf_head2 = conf_head2.reshape(conf_head2.shape[0], -1)
-                    conf_mask_head2 = conf_head2 > conf_thre
+                    # set conf thre to be 10 percentile of the conf_head2, for each frame
+                    conf_thre = np.percentile(conf_head2.astype(np.float32), conf_thre_percentile, axis=1)
+                    conf_mask_head2 = conf_head2 > conf_thre[:, None]
 
     # Add visualization controls
     with server.gui.add_folder("Visualization"):
@@ -328,10 +332,13 @@ def visualize_st4rtrack(
     frame_nodes_head1: list[viser.FrameHandle] = []
     frame_nodes_head2: list[viser.FrameHandle] = []
 
-    # Color tinting
+    # Extract RGB components for tinting
     blue_r, blue_g, blue_b = blue_rgb
     red_r, red_g, red_b = red_rgb
-
+    
+    # Create frames for each timestep
+    frame_nodes_head1 = []
+    frame_nodes_head2 = []
     for i in tqdm(range(num_frames)):
         # Process head1
         if traj_3d_head1 is not None:
@@ -346,7 +353,6 @@ def visualize_st4rtrack(
             color_head1 = color.copy()
             if gui_use_color_tint.value:
                 color_head1 *= blend_ratio
-
                 color_head1[:, 0] = onp.clip(color_head1[:, 0] + blue_r * (1 - blend_ratio), 0, 1)  # R
                 color_head1[:, 1] = onp.clip(color_head1[:, 1] + blue_g * (1 - blend_ratio), 0, 1)  # G
                 color_head1[:, 2] = onp.clip(color_head1[:, 2] + blue_b * (1 - blend_ratio), 0, 1)  # B
@@ -372,7 +378,6 @@ def visualize_st4rtrack(
             color_head2 = color.copy()
             if gui_use_color_tint.value:
                 color_head2 *= blend_ratio
-
                 color_head2[:, 0] = onp.clip(color_head2[:, 0] + red_r * (1 - blend_ratio), 0, 1)  # R
                 color_head2[:, 1] = onp.clip(color_head2[:, 1] + red_g * (1 - blend_ratio), 0, 1)  # G
                 color_head2[:, 2] = onp.clip(color_head2[:, 2] + red_b * (1 - blend_ratio), 0, 1)  # B
@@ -422,8 +427,11 @@ def visualize_st4rtrack(
         num_points = xyz_head1.shape[1]
         points_to_visualize = min(num_points, num_traj_points)
         
-        # Get the mask for the last frame and reshape it to match point cloud dimensions
-        last_frame_mask = masks[traj_end_frame-1].reshape(-1)
+        # Get the mask for the first frame and reshape it to match point cloud dimensions
+        if mid_anchor:
+            first_frame_mask = masks[num_frames//2].reshape(-1)
+        else:
+            first_frame_mask = masks[0].reshape(-1) #[#points, h]
         
         # Calculate trajectory lengths for each point
         trajectories = xyz_head1_centered[traj_start_frame:traj_end_frame]  # Shape: (num_frames, num_points, 3)
@@ -431,15 +439,19 @@ def visualize_st4rtrack(
         traj_lengths = np.sum(np.sqrt(np.sum(traj_diffs**2, axis=-1)), axis=0)  # Sum of distances for each point
         
         # Get points that are within the mask
-        valid_indices = np.where(last_frame_mask)[0]
+        valid_indices = np.where(first_frame_mask)[0]
         
         if len(valid_indices) > 0:
             # Calculate average trajectory length for masked points
             masked_traj_lengths = traj_lengths[valid_indices]
             avg_traj_length = np.mean(masked_traj_lengths)
             
-            # Filter points by trajectory length
-            long_traj_indices = valid_indices[masked_traj_lengths >= avg_traj_length]
+            if mask_folder is not None:
+                # do not filter points by trajectory length
+                long_traj_indices = valid_indices
+            else:
+                # Filter points by trajectory length
+                long_traj_indices = valid_indices[masked_traj_lengths >= avg_traj_length]
             
             # Randomly sample from the filtered points
             if len(long_traj_indices) > 0:
@@ -591,7 +603,6 @@ def visualize_st4rtrack(
                     color_head1 = color.copy()
                     if gui_use_color_tint.value:
                         color_head1 *= blend_ratio
-
                         color_head1[:, 0] = onp.clip(color_head1[:, 0] + blue_r * (1 - blend_ratio), 0, 1)  # R
                         color_head1[:, 1] = onp.clip(color_head1[:, 1] + blue_g * (1 - blend_ratio), 0, 1)  # G
                         color_head1[:, 2] = onp.clip(color_head1[:, 2] + blue_b * (1 - blend_ratio), 0, 1)  # B
@@ -616,7 +627,6 @@ def visualize_st4rtrack(
                     color_head2 = color.copy()
                     if gui_use_color_tint.value:
                         color_head2 *= blend_ratio
-
                         color_head2[:, 0] = onp.clip(color_head2[:, 0] + red_r * (1 - blend_ratio), 0, 1)  # R
                         color_head2[:, 1] = onp.clip(color_head2[:, 1] + red_g * (1 - blend_ratio), 0, 1)  # G
                         color_head2[:, 2] = onp.clip(color_head2[:, 2] + red_b * (1 - blend_ratio), 0, 1)  # B