diff --git a/botorch/acquisition/input_constructors.py b/botorch/acquisition/input_constructors.py
index 0714f79fec..4000e5ddcf 100644
--- a/botorch/acquisition/input_constructors.py
+++ b/botorch/acquisition/input_constructors.py
@@ -62,6 +62,10 @@
     qExpectedHypervolumeImprovement,
     qNoisyExpectedHypervolumeImprovement,
 )
+from botorch.acquisition.multi_objective.hypervolume_knowledge_gradient import (
+    _get_hv_value_function,
+    qHypervolumeKnowledgeGradient,
+)
 from botorch.acquisition.multi_objective.logei import (
     qLogExpectedHypervolumeImprovement,
     qLogNoisyExpectedHypervolumeImprovement,
@@ -1270,6 +1274,56 @@ def construct_inputs_qKG(
     return inputs_qkg
 
 
+@acqf_input_constructor(qHypervolumeKnowledgeGradient)
+def construct_inputs_qHVKG(
+    model: Model,
+    training_data: MaybeDict[SupervisedDataset],
+    bounds: list[tuple[float, float]],
+    objective_thresholds: Tensor,
+    objective: Optional[MCMultiOutputObjective] = None,
+    posterior_transform: Optional[PosteriorTransform] = None,
+    num_fantasies: int = 8,
+    num_pareto: int = 10,
+    **optimize_objective_kwargs: TOptimizeObjectiveKwargs,
+) -> dict[str, Any]:
+    r"""Construct kwargs for `qKnowledgeGradient` constructor."""
+
+    X = _get_dataset_field(training_data, "X", first_only=True)
+    _bounds = torch.as_tensor(bounds, dtype=X.dtype, device=X.device)
+
+    if objective is None:
+        ref_point = objective_thresholds
+    elif isinstance(objective, RiskMeasureMCObjective):
+        ref_point = objective.preprocessing_function(objective_thresholds)
+    else:
+        ref_point = objective(objective_thresholds)
+
+    acq_function = _get_hv_value_function(
+        model=model,
+        ref_point=ref_point,
+        use_posterior_mean=True,
+    )
+
+    _, current_value = optimize_objective(
+        model=model,
+        bounds=_bounds.t(),
+        q=num_pareto,
+        acq_function=acq_function,
+        objective=objective,
+        posterior_transform=posterior_transform,
+        **optimize_objective_kwargs,
+    )
+
+    return {
+        "model": model,
+        "objective": objective,
+        "ref_point": ref_point,
+        "num_fantasies": num_fantasies,
+        "num_pareto": num_pareto,
+        "current_value": current_value.detach().cpu().max(),
+    }
+
+
 @acqf_input_constructor(qMultiFidelityKnowledgeGradient)
 def construct_inputs_qMFKG(
     model: Model,
diff --git a/test/acquisition/test_input_constructors.py b/test/acquisition/test_input_constructors.py
index 3d2abf13df..9cf925f516 100644
--- a/test/acquisition/test_input_constructors.py
+++ b/test/acquisition/test_input_constructors.py
@@ -72,6 +72,10 @@
     qExpectedHypervolumeImprovement,
     qNoisyExpectedHypervolumeImprovement,
 )
+from botorch.acquisition.multi_objective.hypervolume_knowledge_gradient import (
+    qHypervolumeKnowledgeGradient,
+)
+
 from botorch.acquisition.multi_objective.logei import (
     qLogExpectedHypervolumeImprovement,
     qLogNoisyExpectedHypervolumeImprovement,
@@ -1291,6 +1295,39 @@ def test_construct_inputs_kg(self) -> None:
             )
             self.assertNotIn("current_value", kwargs)
 
+    def test_construct_inputs_hvkg(self) -> None:
+        current_value = torch.tensor(1.23)
+        objective_thresholds = torch.rand(2)
+
+        with mock.patch(
+            target="botorch.acquisition.input_constructors.optimize_acqf",
+            return_value=(None, current_value),
+        ):
+            get_kwargs = get_acqf_input_constructor(qHypervolumeKnowledgeGradient)
+            kwargs = get_kwargs(
+                model=mock.Mock(),
+                training_data=self.blockX_blockY,
+                objective_thresholds=objective_thresholds,
+                bounds=self.bounds,
+                num_fantasies=33,
+                num_pareto=11,
+            )
+            self.assertLessEqual(
+                {
+                    "model",
+                    "ref_point",
+                    "num_fantasies",
+                    "num_pareto",
+                    "objective",
+                    "current_value",
+                },
+                set(kwargs.keys()),
+            )
+            self.assertEqual(kwargs["num_fantasies"], 33)
+            self.assertEqual(kwargs["num_pareto"], 11)
+            self.assertEqual(kwargs["current_value"], current_value)
+            self.assertTrue(torch.equal(kwargs["ref_point"], objective_thresholds))
+
     def test_construct_inputs_mes(self) -> None:
         func = get_acqf_input_constructor(qMaxValueEntropy)
         n, d, m = 5, 2, 1
@@ -1603,6 +1640,23 @@ def setUp(self, suppress_input_warnings: bool = True) -> None:
                 "training_data": self.blockX_blockY,
             },
         )
+
+        X = torch.rand(3, 2)
+        Y1 = torch.rand(3, 1)
+        Y2 = torch.rand(3, 1)
+        m1 = SingleTaskGP(X, Y1)
+        m2 = SingleTaskGP(X, Y2)
+        model_list = ModelListGP(m1, m2)
+
+        self.cases["HV Look-ahead"] = (
+            [qHypervolumeKnowledgeGradient],
+            {
+                "model": model_list,
+                "training_data": self.blockX_blockY,
+                "bounds": bounds,
+                "objective_thresholds": objective_thresholds,
+            },
+        )
         pref_model = self.mock_model
         pref_model.dim = 2
         pref_model.datapoints = torch.tensor([])