diff --git a/pandas/core/reshape/reshape.py b/pandas/core/reshape/reshape.py
index 0a2f7fe43b4b3..574e6839070be 100644
--- a/pandas/core/reshape/reshape.py
+++ b/pandas/core/reshape/reshape.py
@@ -35,6 +35,7 @@
     factorize,
     unique,
 )
+from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
 from pandas.core.arrays.categorical import factorize_from_iterable
 from pandas.core.construction import ensure_wrapped_if_datetimelike
 from pandas.core.frame import DataFrame
@@ -231,20 +232,31 @@ def arange_result(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.bool_]]:
         return new_values, mask.any(0)
         # TODO: in all tests we have mask.any(0).all(); can we rely on that?
 
-    def get_result(self, values, value_columns, fill_value) -> DataFrame:
+    def get_result(self, obj, value_columns, fill_value) -> DataFrame:
+        values = obj._values
         if values.ndim == 1:
             values = values[:, np.newaxis]
 
         if value_columns is None and values.shape[1] != 1:  # pragma: no cover
             raise ValueError("must pass column labels for multi-column data")
 
-        values, _ = self.get_new_values(values, fill_value)
+        new_values, _ = self.get_new_values(values, fill_value)
         columns = self.get_new_columns(value_columns)
         index = self.new_index
 
-        return self.constructor(
-            values, index=index, columns=columns, dtype=values.dtype
+        result = self.constructor(
+            new_values, index=index, columns=columns, dtype=new_values.dtype, copy=False
         )
+        if isinstance(values, np.ndarray):
+            base, new_base = values.base, new_values.base
+        elif isinstance(values, NDArrayBackedExtensionArray):
+            base, new_base = values._ndarray.base, new_values._ndarray.base
+        else:
+            base, new_base = 1, 2  # type: ignore[assignment]
+        if base is new_base:
+            # We can only get here if one of the dimensions is size 1
+            result._mgr.add_references(obj._mgr)
+        return result
 
     def get_new_values(self, values, fill_value=None):
         if values.ndim == 1:
@@ -532,9 +544,7 @@ def unstack(
         unstacker = _Unstacker(
             obj.index, level=level, constructor=obj._constructor_expanddim, sort=sort
         )
-        return unstacker.get_result(
-            obj._values, value_columns=None, fill_value=fill_value
-        )
+        return unstacker.get_result(obj, value_columns=None, fill_value=fill_value)
 
 
 def _unstack_frame(
@@ -550,7 +560,7 @@ def _unstack_frame(
         return obj._constructor_from_mgr(mgr, axes=mgr.axes)
     else:
         return unstacker.get_result(
-            obj._values, value_columns=obj.columns, fill_value=fill_value
+            obj, value_columns=obj.columns, fill_value=fill_value
         )
 
 
diff --git a/pandas/tests/reshape/test_pivot.py b/pandas/tests/reshape/test_pivot.py
index f750d5e7fa919..2ccb622c7a250 100644
--- a/pandas/tests/reshape/test_pivot.py
+++ b/pandas/tests/reshape/test_pivot.py
@@ -2703,3 +2703,16 @@ def test_pivot_table_with_margins_and_numeric_column_names(self):
             index=Index(["a", "b", "All"], name=0),
         )
         tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("m", [1, 10])
+    def test_unstack_shares_memory(self, m):
+        # GH#56633
+        levels = np.arange(m)
+        index = MultiIndex.from_product([levels] * 2)
+        values = np.arange(m * m * 100).reshape(m * m, 100)
+        df = DataFrame(values, index, np.arange(100))
+        df_orig = df.copy()
+        result = df.unstack(sort=False)
+        assert np.shares_memory(df._values, result._values) is (m == 1)
+        result.iloc[0, 0] = -1
+        tm.assert_frame_equal(df, df_orig)