diff --git a/pytorch3d/transforms/transform3d.py b/pytorch3d/transforms/transform3d.py
index c954e749..23a8aa26 100644
--- a/pytorch3d/transforms/transform3d.py
+++ b/pytorch3d/transforms/transform3d.py
@@ -434,7 +434,7 @@ class Transform3d:
 
 
 class Translate(Transform3d):
-    def __init__(self, x, y=None, z=None, dtype=torch.float32, device="cpu"):
+    def __init__(self, x, y=None, z=None, dtype=torch.float32, device=None):
         """
         Create a new Transform3d representing 3D translations.
 
@@ -448,11 +448,11 @@ class Translate(Transform3d):
                 - A torch scalar
                 - A 1D torch tensor
         """
-        super().__init__(device=device)
         xyz = _handle_input(x, y, z, dtype, device, "Translate")
+        super().__init__(device=xyz.device)
         N = xyz.shape[0]
 
-        mat = torch.eye(4, dtype=dtype, device=device)
+        mat = torch.eye(4, dtype=dtype, device=self.device)
         mat = mat.view(1, 4, 4).repeat(N, 1, 1)
         mat[:, 3, :3] = xyz
         self._matrix = mat
@@ -468,7 +468,7 @@ class Translate(Transform3d):
 
 
 class Scale(Transform3d):
-    def __init__(self, x, y=None, z=None, dtype=torch.float32, device="cpu"):
+    def __init__(self, x, y=None, z=None, dtype=torch.float32, device=None):
         """
         A Transform3d representing a scaling operation, with different scale
         factors along each coordinate axis.
@@ -485,12 +485,12 @@ class Scale(Transform3d):
                 - torch scalar
                 - 1D torch tensor
         """
-        super().__init__(device=device)
         xyz = _handle_input(x, y, z, dtype, device, "scale", allow_singleton=True)
+        super().__init__(device=xyz.device)
         N = xyz.shape[0]
 
         # TODO: Can we do this all in one go somehow?
-        mat = torch.eye(4, dtype=dtype, device=device)
+        mat = torch.eye(4, dtype=dtype, device=self.device)
         mat = mat.view(1, 4, 4).repeat(N, 1, 1)
         mat[:, 0, 0] = xyz[:, 0]
         mat[:, 1, 1] = xyz[:, 1]
@@ -509,7 +509,7 @@ class Scale(Transform3d):
 
 class Rotate(Transform3d):
     def __init__(
-        self, R, dtype=torch.float32, device="cpu", orthogonal_tol: float = 1e-5
+        self, R, dtype=torch.float32, device=None, orthogonal_tol: float = 1e-5
     ):
         """
         Create a new Transform3d representing 3D rotation using a rotation
@@ -520,6 +520,7 @@ class Rotate(Transform3d):
             orthogonal_tol: tolerance for the test of the orthogonality of R
 
         """
+        device = _get_device(R, device)
         super().__init__(device=device)
         if R.dim() == 2:
             R = R[None]
@@ -548,7 +549,7 @@ class RotateAxisAngle(Rotate):
         axis: str = "X",
         degrees: bool = True,
         dtype=torch.float64,
-        device="cpu",
+        device=None,
     ):
         """
         Create a new Transform3d representing 3D rotation about an axis
@@ -578,7 +579,7 @@ class RotateAxisAngle(Rotate):
         # is for transforming column vectors. Therefore we transpose this matrix.
         # R will always be of shape (N, 3, 3)
         R = _axis_angle_rotation(axis, angle).transpose(1, 2)
-        super().__init__(device=device, R=R)
+        super().__init__(device=angle.device, R=R)
 
 
 def _handle_coord(c, dtype, device):
@@ -595,9 +596,24 @@ def _handle_coord(c, dtype, device):
         c = torch.tensor(c, dtype=dtype, device=device)
     if c.dim() == 0:
         c = c.view(1)
+    if c.device != device:
+        c = c.to(device=device)
     return c
 
 
+def _get_device(x, device=None):
+    if device is not None:
+        # User overriding device, leave
+        device = device
+    elif torch.is_tensor(x):
+        # Set device based on input tensor
+        device = x.device
+    else:
+        # Default device is cpu
+        device = "cpu"
+    return device
+
+
 def _handle_input(x, y, z, dtype, device, name: str, allow_singleton: bool = False):
     """
     Helper function to handle parsing logic for building transforms. The output
@@ -626,6 +642,7 @@ def _handle_input(x, y, z, dtype, device, name: str, allow_singleton: bool = Fal
     Returns:
         xyz: Tensor of shape (N, 3)
     """
+    device = _get_device(x, device)
     # If x is actually a tensor of shape (N, 3) then just return it
     if torch.is_tensor(x) and x.dim() == 2:
         if x.shape[1] != 3:
@@ -634,7 +651,7 @@ def _handle_input(x, y, z, dtype, device, name: str, allow_singleton: bool = Fal
         if y is not None or z is not None:
             msg = "Expected y and z to be None (in %s)" % name
             raise ValueError(msg)
-        return x
+        return x.to(device=device)
 
     if allow_singleton and y is None and z is None:
         y = x
@@ -665,6 +682,7 @@ def _handle_angle_input(x, dtype, device, name: str):
         - Python scalar
         - Torch scalar
     """
+    device = _get_device(x, device)
     if torch.is_tensor(x) and x.dim() > 1:
         msg = "Expected tensor of shape (N,); got %r (in %s)"
         raise ValueError(msg % (x.shape, name))