diff --git a/test/test_transforms_v2_refactored.py b/test/test_transforms_v2_refactored.py
index 56eb6747c6b..c83327a069e 100644
--- a/test/test_transforms_v2_refactored.py
+++ b/test/test_transforms_v2_refactored.py
@@ -2259,3 +2259,99 @@ def test_image_correctness(self, permutation, batch_dims):
         expected = self.reference_image_correctness(image, permutation=permutation)
 
         torch.testing.assert_close(actual, expected)
+
+
+class TestElastic:
+    def _make_displacement(self, inpt):
+        return torch.rand(
+            1,
+            *F.get_size(inpt),
+            2,
+            dtype=torch.float32,
+            device=inpt.device if isinstance(inpt, torch.Tensor) else "cpu",
+        )
+
+    @param_value_parametrization(
+        interpolation=[transforms.InterpolationMode.NEAREST, transforms.InterpolationMode.BILINEAR],
+        fill=EXHAUSTIVE_TYPE_FILLS,
+    )
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    def test_kernel_image_tensor(self, param, value, dtype, device):
+        image = make_image_tensor(dtype=dtype, device=device)
+
+        check_kernel(
+            F.elastic_image_tensor,
+            image,
+            displacement=self._make_displacement(image),
+            **{param: value},
+            check_scripted_vs_eager=not (param == "fill" and isinstance(value, (int, float))),
+        )
+
+    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.int64])
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    def test_kernel_bounding_boxes(self, format, dtype, device):
+        bounding_boxes = make_bounding_box(format=format, dtype=dtype, device=device)
+
+        check_kernel(
+            F.elastic_bounding_boxes,
+            bounding_boxes,
+            format=bounding_boxes.format,
+            canvas_size=bounding_boxes.canvas_size,
+            displacement=self._make_displacement(bounding_boxes),
+        )
+
+    @pytest.mark.parametrize("make_mask", [make_segmentation_mask, make_detection_mask])
+    def test_kernel_mask(self, make_mask):
+        mask = make_mask()
+        check_kernel(F.elastic_mask, mask, displacement=self._make_displacement(mask))
+
+    def test_kernel_video(self):
+        video = make_video()
+        check_kernel(F.elastic_video, video, displacement=self._make_displacement(video))
+
+    @pytest.mark.parametrize(
+        "make_input",
+        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
+    )
+    def test_functional(self, make_input):
+        input = make_input()
+        check_functional(F.elastic, input, displacement=self._make_displacement(input))
+
+    @pytest.mark.parametrize(
+        ("kernel", "input_type"),
+        [
+            (F.elastic_image_tensor, torch.Tensor),
+            (F.elastic_image_pil, PIL.Image.Image),
+            (F.elastic_image_tensor, datapoints.Image),
+            (F.elastic_bounding_boxes, datapoints.BoundingBoxes),
+            (F.elastic_mask, datapoints.Mask),
+            (F.elastic_video, datapoints.Video),
+        ],
+    )
+    def test_functional_signature(self, kernel, input_type):
+        check_functional_kernel_signature_match(F.elastic, kernel=kernel, input_type=input_type)
+
+    @pytest.mark.parametrize(
+        "make_input",
+        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
+    )
+    def test_displacement_error(self, make_input):
+        input = make_input()
+
+        with pytest.raises(TypeError, match="displacement should be a Tensor"):
+            F.elastic(input, displacement=None)
+
+        with pytest.raises(ValueError, match="displacement shape should be"):
+            F.elastic(input, displacement=torch.rand(F.get_size(input)))
+
+    @pytest.mark.parametrize(
+        "make_input",
+        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
+    )
+    # ElasticTransform needs larger images to avoid the needed internal padding being larger than the actual image
+    @pytest.mark.parametrize("size", [(163, 163), (72, 333), (313, 95)])
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    def test_transform(self, make_input, size, device):
+        check_transform(transforms.ElasticTransform, make_input(size, device=device))
diff --git a/torchvision/transforms/v2/functional/_geometry.py b/torchvision/transforms/v2/functional/_geometry.py
index 0872d71dd8e..898e7e0c1a8 100644
--- a/torchvision/transforms/v2/functional/_geometry.py
+++ b/torchvision/transforms/v2/functional/_geometry.py
@@ -1755,6 +1755,9 @@ def elastic_image_tensor(
     interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
     fill: _FillTypeJIT = None,
 ) -> torch.Tensor:
+    if not isinstance(displacement, torch.Tensor):
+        raise TypeError("Argument displacement should be a Tensor")
+
     interpolation = _check_interpolation(interpolation)
 
     if image.numel() == 0:
@@ -1835,6 +1838,12 @@ def elastic_bounding_boxes(
     canvas_size: Tuple[int, int],
     displacement: torch.Tensor,
 ) -> torch.Tensor:
+    expected_shape = (1, canvas_size[0], canvas_size[1], 2)
+    if not isinstance(displacement, torch.Tensor):
+        raise TypeError("Argument displacement should be a Tensor")
+    elif displacement.shape != expected_shape:
+        raise ValueError(f"Argument displacement shape should be {expected_shape}, but given {displacement.shape}")
+
     if bounding_boxes.numel() == 0:
         return bounding_boxes