diff --git a/pytorch360convert/pytorch360convert.py b/pytorch360convert/pytorch360convert.py
index e65a3d2..a7b25d1 100644
--- a/pytorch360convert/pytorch360convert.py
+++ b/pytorch360convert/pytorch360convert.py
@@ -514,9 +514,10 @@ def sample_cubefaces(
     # coor_x, coor_y and tp.
     cube_faces_mod = cube_faces.clone()
 
-    face_w = cube_faces_mod.shape[1]
+    d = 1 if cube_faces.dim() == 4 else 2
+    face_w = cube_faces_mod.shape[d]
     cube_h = torch.cat(
-        [cube_faces_mod[i] for i in range(6)], dim=1
+        [cube_faces_mod[i] for i in range(6)], dim=d
     )  # [face_w, face_w*6, C]
 
     # We need to map (tp, coor_y, coor_x) -> coordinates in cube_h
@@ -752,38 +753,40 @@ def c2e(
     # Ensure input is in HWC format for processing
     if channels_first:
         if cube_format == "list" and isinstance(cubemap, (list, tuple)):
-            cubemap = [r.permute(1, 2, 0) for r in cubemap]
+            cubemap = [_nchw2nhwc(r) for r in cubemap]
         elif cube_format == "dict" and torch.jit.isinstance(
             cubemap, Dict[str, torch.Tensor]
         ):
-            cubemap = {k: v.permute(1, 2, 0) for k, v in cubemap.items()}  # type: ignore
+            cubemap = {k: _nchw2nhwc(v) for k, v in cubemap.items()}  # type: ignore
         elif cube_format in ["horizon", "dice"] and isinstance(cubemap, torch.Tensor):
-            cubemap = cubemap.permute(1, 2, 0)
+            cubemap = _nchw2nhwc(cubemap)
         else:
             raise NotImplementedError("unknown cube_format and cubemap type")
 
     if cube_format == "horizon" and isinstance(cubemap, torch.Tensor):
-        assert cubemap.dim() == 3
+        assert cubemap.dim() in [3, 4]
         cube_h = cubemap
     elif cube_format == "list" and isinstance(cubemap, (list, tuple)):
-        assert all([r.dim() == 3 for r in cubemap])
+        assert all([r.dim() in [3, 4] for r in cubemap])
         cube_h = cube_list2h(cubemap)
     elif cube_format == "dict" and torch.jit.isinstance(
         cubemap, Dict[str, torch.Tensor]
     ):
-        assert all(v.dim() == 3 for k, v in cubemap.items())  # type: ignore[union-attr]
+        assert all(v.dim() in [3, 4] for k, v in cubemap.items())  # type: ignore[union-attr]
         cube_h = cube_dict2h(cubemap)  # type: ignore[arg-type]
     elif cube_format == "dice" and isinstance(cubemap, torch.Tensor):
-        assert len(cubemap.shape) == 3
+        assert len(cubemap.shape) in [3, 4]
         cube_h = cube_dice2h(cubemap)
     else:
         raise NotImplementedError("unknown cube_format and cubemap type")
     assert isinstance(cube_h, torch.Tensor)  # Mypy wants this
 
+    # cube_h -> B, H, W, C
+    d = 1 if cube_h.dim() == 3 else 2
     device = cube_h.device
     dtype = cube_h.dtype
-    face_w = cube_h.shape[0]
-    assert cube_h.shape[1] == face_w * 6
+    face_w = cube_h.shape[d - 1]
+    assert cube_h.shape[d] == face_w * 6
 
     h = face_w * 2 if h is None else h
     w = face_w * 4 if w is None else w
@@ -792,7 +795,7 @@ def c2e(
     u, v = uv[..., 0], uv[..., 1]
 
     cube_faces = torch.stack(
-        torch.split(cube_h, face_w, dim=1), dim=0
+        torch.split(cube_h, face_w, dim=d), dim=0
     )  # [6, face_w, face_w, C]
 
     tp = equirect_facetype(h, w, device=device, dtype=dtype)