diff --git a/python/tvm/topi/image/resize.py b/python/tvm/topi/image/resize.py
index 8d0a984982e1..69f3fa85eeb8 100644
--- a/python/tvm/topi/image/resize.py
+++ b/python/tvm/topi/image/resize.py
@@ -152,7 +152,8 @@ def get_inx(
     if coordinate_transformation_mode == "half_pixel":
         in_x = (x + 0.5) * scale_x - 0.5
     elif coordinate_transformation_mode == "align_corners":
-        in_x = (image_width - 1).astype("float") / (target_width - 1) * x
+        # A singleton output has only x=0, which maps to the first input sample.
+        in_x = (image_width - 1).astype("float") / te.max(target_width - 1, 1) * x
     elif coordinate_transformation_mode == "asymmetric":
         if use_int_div:
             in_x = te.div(x, te.div(target_width, image_width))
diff --git a/tests/python/relax/test_frontend_from_exported_program.py b/tests/python/relax/test_frontend_from_exported_program.py
index ef266b2be05a..233b44e4076d 100644
--- a/tests/python/relax/test_frontend_from_exported_program.py
+++ b/tests/python/relax/test_frontend_from_exported_program.py
@@ -4200,6 +4200,34 @@ def main(data: R.Tensor((3, 1, 3), dtype="float32")) -> R.Tuple(
     verify_model(Unbind2(), single_dim_args, {}, expected3)
 
 
+@pytest.mark.skipif(not device_enabled("llvm"), reason="LLVM is required")
+@pytest.mark.parametrize(
+    "input_hw, size, scale_factor, align_corners",
+    [
+        ((4, 4), (1, 6), None, True),
+        ((4, 4), (6, 1), None, True),
+        ((4, 4), (1, 1), None, True),
+        ((1, 4), None, 1.5, True),
+        ((4, 4), (2, 3), None, True),
+        ((4, 4), (1, 6), None, False),
+    ],
+)
+def test_interpolate_bilinear_singleton_axis(input_hw, size, scale_factor, align_corners):
+    class Interpolate(Module):
+        def forward(self, x):
+            return torch.nn.functional.interpolate(
+                x,
+                size=size,
+                scale_factor=scale_factor,
+                mode="bilinear",
+                align_corners=align_corners,
+            )
+
+    height, width = input_hw
+    x = torch.arange(height * width, dtype=torch.float32).reshape(1, 1, height, width)
+    verify_model_numerically(Interpolate(), (x,), rtol=1e-6, atol=1e-6)
+
+
 def test_interpolate():
     class InterpolateBilinear(Module):
         def forward(self, input):
diff --git a/tests/python/te/test_te_create_function.py b/tests/python/te/test_te_create_function.py
index 9c1fa4ad5129..55592337620d 100644
--- a/tests/python/te/test_te_create_function.py
+++ b/tests/python/te/test_te_create_function.py
@@ -808,6 +808,23 @@ def test_resize2d_symbolic():
     _check_workload(te_resize2d_symbolic, tir_resize2d_symbolic, index_dtype_override="int64")
 
 
+@pytest.mark.skipif(not tvm_tirx.testing.device_enabled("llvm"), reason="LLVM is required")
+def test_resize1d_align_corners_symbolic_width():
+    width = tirx.Var("width", "int64")
+    data = te.placeholder((1, 1, 5), "float32", name="data")
+    resized = topi.image.resize1d(
+        data, (0.0, 0.0), (width,), coordinate_transformation_mode="align_corners"
+    )
+    resize = tvm.compile(te.create_function([data, resized]), target="llvm")
+
+    values = tvm_tirx.runtime.tensor(np.arange(5, dtype="float32").reshape(1, 1, 5))
+    for output_width in (1, 3):
+        actual = tvm_tirx.runtime.empty((1, 1, output_width), "float32")
+        resize(values, actual)
+        expected = np.linspace(0, 4, output_width, dtype="float32").reshape(1, 1, output_width)
+        np.testing.assert_array_equal(actual.numpy(), expected, strict=True)
+
+
 def test_extern_with_explicit_buffer_access():
     def te_extern():
         A = te.placeholder((128, 128), name="A")