diff --git a/bigframes/core/blocks.py b/bigframes/core/blocks.py
index 239eedf6d3..fff03c9310 100644
--- a/bigframes/core/blocks.py
+++ b/bigframes/core/blocks.py
@@ -1822,9 +1822,9 @@ def melt(
         Arguments correspond to pandas.melt arguments.
         """
         # TODO: Implement col_level and ignore_index
-        value_labels: pd.Index = pd.Index(
-            [self.col_id_to_label[col_id] for col_id in value_vars]
-        )
+        value_labels: pd.Index = self.column_labels[
+            [self.value_columns.index(col_id) for col_id in value_vars]
+        ]
         id_labels = [self.col_id_to_label[col_id] for col_id in id_vars]
 
         unpivot_expr, (var_col_ids, unpivot_out, passthrough_cols) = unpivot(
@@ -3417,6 +3417,7 @@ def unpivot(
         joined_array, (labels_mapping, column_mapping) = labels_array.relational_join(
             array_value, type="cross"
         )
+
     new_passthrough_cols = [column_mapping[col] for col in passthrough_columns]
     # Last column is offsets
     index_col_ids = [labels_mapping[col] for col in labels_array.column_ids[:-1]]
@@ -3426,20 +3427,24 @@ def unpivot(
     unpivot_exprs: List[ex.Expression] = []
     # Supports producing multiple stacked ouput columns for stacking only part of hierarchical index
     for input_ids in unpivot_columns:
-        # row explode offset used to choose the input column
-        # we use offset instead of label as labels are not necessarily unique
-        cases = itertools.chain(
-            *(
-                (
-                    ops.eq_op.as_expr(explode_offsets_id, ex.const(i)),
-                    ex.deref(column_mapping[id_or_null])
-                    if (id_or_null is not None)
-                    else ex.const(None),
+        col_expr: ex.Expression
+        if not input_ids:
+            col_expr = ex.const(None)
+        else:
+            # row explode offset used to choose the input column
+            # we use offset instead of label as labels are not necessarily unique
+            cases = itertools.chain(
+                *(
+                    (
+                        ops.eq_op.as_expr(explode_offsets_id, ex.const(i)),
+                        ex.deref(column_mapping[id_or_null])
+                        if (id_or_null is not None)
+                        else ex.const(None),
+                    )
+                    for i, id_or_null in enumerate(input_ids)
                 )
-                for i, id_or_null in enumerate(input_ids)
             )
-        )
-        col_expr = ops.case_when_op.as_expr(*cases)
+            col_expr = ops.case_when_op.as_expr(*cases)
         unpivot_exprs.append(col_expr)
 
     joined_array, unpivot_col_ids = joined_array.compute_values(unpivot_exprs)
@@ -3457,19 +3462,47 @@ def _pd_index_to_array_value(
     Create an ArrayValue from a list of label tuples.
     The last column will be row offsets.
     """
+    id_gen = bigframes.core.identifiers.standard_id_strings()
+    col_ids = [next(id_gen) for _ in range(index.nlevels)]
+    offset_id = next(id_gen)
+
     rows = []
     labels_as_tuples = utils.index_as_tuples(index)
     for row_offset in range(len(index)):
-        id_gen = bigframes.core.identifiers.standard_id_strings()
         row_label = labels_as_tuples[row_offset]
         row_label = (row_label,) if not isinstance(row_label, tuple) else row_label
         row = {}
-        for label_part, id in zip(row_label, id_gen):
-            row[id] = label_part if pd.notnull(label_part) else None
-        row[next(id_gen)] = row_offset
+        for label_part, col_id in zip(row_label, col_ids):
+            row[col_id] = label_part if pd.notnull(label_part) else None
+        row[offset_id] = row_offset
         rows.append(row)
 
-    return core.ArrayValue.from_pyarrow(pa.Table.from_pylist(rows), session=session)
+    import pyarrow as pa
+
+    if not rows:
+        from bigframes.dtypes import bigframes_dtype_to_arrow_dtype
+
+        dtypes_list = getattr(index, "dtypes", None)
+        if dtypes_list is None:
+            dtypes_list = (
+                [index.dtype] if hasattr(index, "dtype") else [pd.Float64Dtype()]
+            )
+
+        fields = []
+        for col_id, dtype in zip(col_ids, dtypes_list):
+            try:
+                pa_type = bigframes_dtype_to_arrow_dtype(dtype)
+            except Exception:
+                pa_type = pa.string()
+            fields.append(pa.field(col_id, pa_type))
+        fields.append(pa.field(offset_id, pa.int64()))
+        schema = pa.schema(fields)
+        pt = pa.Table.from_pylist([], schema=schema)
+    else:
+        pt = pa.Table.from_pylist(rows)
+        pt = pt.rename_columns([*col_ids, offset_id])
+
+    return core.ArrayValue.from_pyarrow(pt, session=session)
 
 
 def _resolve_index_col(
diff --git a/tests/system/small/test_dataframe.py b/tests/system/small/test_dataframe.py
index 9683a8bc52..c9f9be880f 100644
--- a/tests/system/small/test_dataframe.py
+++ b/tests/system/small/test_dataframe.py
@@ -5902,6 +5902,18 @@ def test_to_gbq_table_labels(scalars_df_index):
     assert table.labels["test"] == "labels"
 
 
+def test_dataframe_melt_multiindex(session):
+    # Tests that `melt` operations via count do not cause MultiIndex drops in Arrow
+    df = pd.DataFrame({"A": [1], "B": ["string"], "C": [3]})
+    df.columns = pd.MultiIndex.from_tuples(
+        [("Group1", "A"), ("Group2", "B"), ("Group1", "C")]
+    )
+    bdf = session.read_pandas(df)
+
+    count_df = bdf.count().to_pandas()
+    assert count_df.shape[0] == 3
+
+
 @pytest.mark.parametrize(
     ("col_names", "ignore_index"),
     [