diff --git a/genesis/utils/geom.py b/genesis/utils/geom.py
index acfd6518..aeddc2fe 100644
--- a/genesis/utils/geom.py
+++ b/genesis/utils/geom.py
@@ -366,11 +366,8 @@ def wxyz_to_xyzw(wxyz):
 def transform_quat_by_quat(v, u):
     if isinstance(v, torch.Tensor) and isinstance(u, torch.Tensor):
         assert v.shape == u.shape, f"{v.shape} != {u.shape}"
-        shape = u.shape
-        u = u.reshape(-1, 4)
-        v = v.reshape(-1, 4)
-        w1, x1, y1, z1 = u[:, 0], u[:, 1], u[:, 2], u[:, 3]
-        w2, x2, y2, z2 = v[:, 0], v[:, 1], v[:, 2], v[:, 3]
+        w1, x1, y1, z1 = u[..., 0], u[..., 1], u[..., 2], u[..., 3]
+        w2, x2, y2, z2 = v[..., 0], v[..., 1], v[..., 2], v[..., 3]
         ww = (z1 + x1) * (x2 + y2)
         yy = (w1 - y1) * (w2 + z2)
         zz = (w1 + y1) * (w2 - z2)
@@ -380,15 +377,12 @@ def transform_quat_by_quat(v, u):
         x = qq - xx + (x1 + w1) * (x2 + w2)
         y = qq - yy + (w1 - x1) * (y2 + z2)
         z = qq - zz + (z1 + y1) * (w2 - x2)
-        quat = torch.stack([w, x, y, z], dim=-1).view(shape)
+        quat = torch.stack([w, x, y, z], dim=-1)
         return quat
     elif isinstance(v, np.ndarray) and isinstance(u, np.ndarray):
         assert v.shape == u.shape, f"{v.shape} != {u.shape}"
-        shape = u.shape
-        u = u.reshape(-1, 4)
-        v = v.reshape(-1, 4)
-        w1, x1, y1, z1 = u[:, 0], u[:, 1], u[:, 2], u[:, 3]
-        w2, x2, y2, z2 = v[:, 0], v[:, 1], v[:, 2], v[:, 3]
+        w1, x1, y1, z1 = u[..., 0], u[..., 1], u[..., 2], u[..., 3]
+        w2, x2, y2, z2 = v[..., 0], v[..., 1], v[..., 2], v[..., 3]
         # This method transforms quat_v by quat_u
         # This is equivalent to quatmul(quat_u, quat_v) or R_u @ R_v
         ww = (z1 + x1) * (x2 + y2)
@@ -400,7 +394,7 @@ def transform_quat_by_quat(v, u):
         x = qq - xx + (x1 + w1) * (x2 + w2)
         y = qq - yy + (w1 - x1) * (y2 + z2)
         z = qq - zz + (z1 + y1) * (w2 - x2)
-        quat = np.stack([w, x, y, z], axis=-1).reshape(shape)
+        quat = np.stack([w, x, y, z], axis=-1)
         return quat
     else:
         gs.raise_exception(f"both of the inputs must be torch.Tensor or np.ndarray. got: {type(v)=} and {type(u)=}")
@@ -639,33 +633,15 @@ def xyz_to_quat(euler_xyz):
 
 def transform_by_quat(v, quat):
     if isinstance(v, torch.Tensor) and isinstance(quat, torch.Tensor):
-        shape = v.shape
-        quat = quat.reshape(-1, 4)
-        v = v.reshape(-1, 3)
-        qvec = quat[:, 1:]
+        qvec = quat[..., 1:]
         t = qvec.cross(v, dim=-1) * 2
-        return v + quat[:, :1] * t + qvec.cross(t, dim=-1)
+        return v + quat[..., :1] * t + qvec.cross(t, dim=-1)
     elif isinstance(v, np.ndarray) and isinstance(quat, np.ndarray):
         return transform_by_R(v, quat_to_R(quat))
     else:
         gs.raise_exception(f"both of the inputs must be torch.Tensor or np.ndarray. got: {type(v)=} and {type(quat)=}")
 
 
-def transform_by_quat_yaw(v, quat):
-    if isinstance(v, torch.Tensor) and isinstance(quat, torch.Tensor):
-        quat_yaw = quat.clone().view(-1, 4)
-        quat_yaw[:, 1:3] = 0.0
-        quat_yaw = normalize(quat_yaw)
-        return transform_by_quat(v, quat_yaw)
-    elif isinstance(v, np.ndarray) and isinstance(quat, np.ndarray):
-        quat_yaw = quat.copy().reshape(-1, 4)
-        quat_yaw[:, 1:3] = 0.0
-        quat_yaw = normalize(quat_yaw)
-        return transform_by_quat(v, quat_yaw)
-    else:
-        gs.raise_exception(f"both of the inputs must be torch.Tensor or np.ndarray. got: {type(v)=} and {type(quat)=}")
-
-
 def axis_angle_to_quat(angle, axis):
     if isinstance(angle, torch.Tensor) and isinstance(axis, torch.Tensor):
         theta = (angle / 2).unsqueeze(-1)