diff --git a/cotracker/predictor.py b/cotracker/predictor.py
index dc2cd96..65875fe 100644
--- a/cotracker/predictor.py
+++ b/cotracker/predictor.py
@@ -23,11 +23,11 @@ class CoTrackerPredictor(torch.nn.Module):
     @torch.no_grad()
     def forward(
         self,
-        video,  # (1, T, 3, H, W)
+        video,  # (B, T, 3, H, W)
         # input prompt types:
         # - None. Dense tracks are computed in this case. You can adjust *query_frame* to compute tracks starting from a specific frame.
         # *backward_tracking=True* will compute tracks in both directions.
-        # - queries. Queried points of shape (1, N, 3) in format (t, x, y) for frame index and pixel coordinates.
+        # - queries. Queried points of shape (B, N, 3) in format (t, x, y) for frame index and pixel coordinates.
         # - grid_size. Grid of N*N points from the first frame. if segm_mask is provided, then computed only for the mask.
         # You can adjust *query_frame* and *backward_tracking* for the regular grid in the same way as for dense tracks.
         queries: torch.Tensor = None,
@@ -120,13 +120,14 @@ class CoTrackerPredictor(torch.nn.Module):
             queries = torch.cat(
                 [torch.ones_like(grid_pts[:, :, :1]) * grid_query_frame, grid_pts],
                 dim=2,
-            )
+            ).repeat(B, 1, 1)
 
         if add_support_grid:
             grid_pts = get_points_on_a_grid(
                 self.support_grid_size, self.interp_shape, device=video.device
             )
             grid_pts = torch.cat([torch.zeros_like(grid_pts[:, :, :1]), grid_pts], dim=2)
+            grid_pts = grid_pts.repeat(B, 1, 1)
             queries = torch.cat([queries, grid_pts], dim=1)
 
         tracks, visibilities, __ = self.model.forward(video=video, queries=queries, iters=6)
@@ -173,7 +174,7 @@ class CoTrackerPredictor(torch.nn.Module):
         inv_visibilities = inv_visibilities.flip(1)
         arange = torch.arange(video.shape[1], device=queries.device)[None, :, None]
 
-        mask = (arange < queries[None, :, :, 0]).unsqueeze(-1).repeat(1, 1, 1, 2)
+        mask = (arange < queries[:, None, :, 0]).unsqueeze(-1).repeat(1, 1, 1, 2)
 
         tracks[mask] = inv_tracks[mask]
         visibilities[mask[:, :, :, 0]] = inv_visibilities[mask[:, :, :, 0]]