diff --git a/torchvision/prototype/transforms/functional/_geometry.py b/torchvision/prototype/transforms/functional/_geometry.py
index d4f1fadb0bf..65673203941 100644
--- a/torchvision/prototype/transforms/functional/_geometry.py
+++ b/torchvision/prototype/transforms/functional/_geometry.py
@@ -401,18 +401,16 @@ def pad_bounding_box(
 ) -> torch.Tensor:
     left, _, top, _ = _FT._parse_pad_padding(padding)
 
-    shape = bounding_box.shape
-
     bounding_box = convert_bounding_box_format(
         bounding_box, old_format=format, new_format=features.BoundingBoxFormat.XYXY
-    ).view(-1, 4)
+    )
 
-    bounding_box[:, 0::2] += left
-    bounding_box[:, 1::2] += top
+    bounding_box[..., 0::2] += left
+    bounding_box[..., 1::2] += top
 
     return convert_bounding_box_format(
         bounding_box, old_format=features.BoundingBoxFormat.XYXY, new_format=format, copy=False
-    ).view(shape)
+    )
 
 
 crop_image_tensor = _FT.crop
@@ -425,19 +423,17 @@ def crop_bounding_box(
     top: int,
     left: int,
 ) -> torch.Tensor:
-    shape = bounding_box.shape
-
     bounding_box = convert_bounding_box_format(
         bounding_box, old_format=format, new_format=features.BoundingBoxFormat.XYXY
-    ).view(-1, 4)
+    )
 
     # Crop or implicit pad if left and/or top have negative values:
-    bounding_box[:, 0::2] -= left
-    bounding_box[:, 1::2] -= top
+    bounding_box[..., 0::2] -= left
+    bounding_box[..., 1::2] -= top
 
     return convert_bounding_box_format(
         bounding_box, old_format=features.BoundingBoxFormat.XYXY, new_format=format, copy=False
-    ).view(shape)
+    )
 
 
 def crop_segmentation_mask(img: torch.Tensor, top: int, left: int, height: int, width: int) -> torch.Tensor: