diff --git a/pytorch3d/transforms/rotation_conversions.py b/pytorch3d/transforms/rotation_conversions.py
index 98b22ca2f..d4c76916e 100644
--- a/pytorch3d/transforms/rotation_conversions.py
+++ b/pytorch3d/transforms/rotation_conversions.py
@@ -114,7 +114,7 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
 
     batch_dim = matrix.shape[:-2]
     m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(
-        matrix.reshape(*batch_dim, 9), dim=-1
+        matrix.reshape(batch_dim + (9,)), dim=-1
     )
 
     q_abs = _sqrt_positive_part(
@@ -142,14 +142,15 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
 
     # We floor here at 0.1 but the exact level is not important; if q_abs is small,
     # the candidate won't be picked.
-    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(q_abs.new_tensor(0.1)))
+    flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device)
+    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr))
 
     # if not for numerical problems, quat_candidates[i] should be same (up to a sign),
     # forall i; we pick the best-conditioned one (with the largest denominator)
 
     return quat_candidates[
         F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :  # pyre-ignore[16]
-    ].reshape(*batch_dim, 4)
+    ].reshape(batch_dim + (4,))
 
 
 def _axis_angle_rotation(axis: str, angle):
@@ -238,13 +239,14 @@ def _angle_from_tan(
     return torch.atan2(data[..., i2], -data[..., i1])
 
 
-def _index_from_letter(letter: str):
+def _index_from_letter(letter: str) -> int:
     if letter == "X":
         return 0
     if letter == "Y":
         return 1
     if letter == "Z":
         return 2
+    raise ValueError("letter must be either X, Y or Z.")
 
 
 def matrix_to_euler_angles(matrix, convention: str):
@@ -573,4 +575,5 @@ def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
     IEEE Conference on Computer Vision and Pattern Recognition, 2019.
     Retrieved from http://arxiv.org/abs/1812.07035
     """
-    return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6)
+    batch_dim = matrix.size()[:-2]
+    return matrix[..., :2, :].clone().reshape(batch_dim + (6,))
diff --git a/tests/test_rotation_conversions.py b/tests/test_rotation_conversions.py
index 85888a455..3a992eb06 100644
--- a/tests/test_rotation_conversions.py
+++ b/tests/test_rotation_conversions.py
@@ -8,6 +8,7 @@
 import itertools
 import math
 import unittest
+from distutils.version import LooseVersion
 from typing import Optional, Union
 
 import numpy as np
@@ -264,6 +265,13 @@ def test_6d(self):
             torch.matmul(r, r.permute(0, 2, 1)), torch.eye(3).expand_as(r), atol=1e-6
         )
 
+    @unittest.skipIf(LooseVersion(torch.__version__) < "1.9", "recent torchscript only")
+    def test_scriptable(self):
+        torch.jit.script(matrix_to_axis_angle),
+        torch.jit.script(matrix_to_euler_angles),
+        torch.jit.script(matrix_to_quaternion),
+        torch.jit.script(matrix_to_rotation_6d),
+
     def _assert_quaternions_close(
         self,
         input: Union[torch.Tensor, np.ndarray],