diff --git a/pytorch3d/transforms/rotation_conversions.py b/pytorch3d/transforms/rotation_conversions.py
index c3320a45..1aec4fc6 100644
--- a/pytorch3d/transforms/rotation_conversions.py
+++ b/pytorch3d/transforms/rotation_conversions.py
@@ -95,13 +95,7 @@ def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
     Returns torch.sqrt(torch.max(0, x))
     but with a zero subgradient where x is 0.
     """
-    ret = torch.zeros_like(x)
-    positive_mask = x > 0
-    if torch.is_grad_enabled():
-        ret[positive_mask] = torch.sqrt(x[positive_mask])
-    else:
-        ret = torch.where(positive_mask, torch.sqrt(x), ret)
-    return ret
+    return torch.sqrt(F.relu(x))
 
 
 def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
@@ -160,9 +154,10 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
 
     # 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)
-    out = quat_candidates[
-        F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :
-    ].reshape(batch_dim + (4,))
+    indices = q_abs.argmax(dim=-1, keepdim=True)
+    expand_dims = list(batch_dim) + [1, 4]
+    gather_indices = indices.unsqueeze(-1).expand(expand_dims)
+    out = torch.gather(quat_candidates, -2, gather_indices).squeeze(-2)
     return standardize_quaternion(out)