diff --git a/onnxscript/function_libs/torch_lib/ops/core.py b/onnxscript/function_libs/torch_lib/ops/core.py
index 5896f13471..e0e4cb68ae 100644
--- a/onnxscript/function_libs/torch_lib/ops/core.py
+++ b/onnxscript/function_libs/torch_lib/ops/core.py
@@ -2127,7 +2127,7 @@ def aten_conv3d(
 
     if bias is None:
         weight_dim_0 = op.Shape(weight, start=0, end=1)
-        bias_shape = op.Concat(weight_dim_0, op.Constant(value_ints=[2]), axis=0)
+        bias_shape = op.Expand(weight_dim_0, op.Constant(value_ints=[1]))
         zero = op.CastLike(0.0, input)
         bias = op.Expand(zero, bias_shape)
 
diff --git a/tests/function_libs/torch_lib/e2e_ops_tests.py b/tests/function_libs/torch_lib/e2e_ops_tests.py
index 440e2316c1..1a393d6fd7 100644
--- a/tests/function_libs/torch_lib/e2e_ops_tests.py
+++ b/tests/function_libs/torch_lib/e2e_ops_tests.py
@@ -5,6 +5,7 @@
 import math
 import unittest
 
+import onnx
 import parameterized
 
 # TODO(pytorch/pytorch#129279): Migrate these tests to the PyTorch repo
@@ -1004,6 +1005,32 @@ def forward(self, x):
                 got = onnx_program.call_reference({"x": inputs[0]})
                 torch.testing.assert_close(expected, got[0])
 
+    def test_conv3d_without_bias_produces_1d_bias(self):
+        class Model(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.conv = torch.nn.Conv3d(3, 4, kernel_size=2, bias=False)
+
+            def forward(self, x):
+                return self.conv(x)
+
+        onnx_program = torch.onnx.export(
+            Model().eval(), (torch.randn(1, 3, 8, 8, 8),), dynamo=True, optimize=False
+        )
+        _testing.assert_onnx_program(onnx_program)
+
+        # The bias synthesized for a bias-less conv must be 1D ([out_channels]) to
+        # match the ONNX Conv spec. See https://github.com/microsoft/onnxscript/issues/2931.
+        inferred = onnx.shape_inference.infer_shapes(onnx_program.model_proto, data_prop=True)
+        shape_ranks = {
+            value_info.name: len(value_info.type.tensor_type.shape.dim)
+            for value_info in inferred.graph.value_info
+            if value_info.type.tensor_type.HasField("shape")
+        }
+        conv_nodes = [node for node in inferred.graph.node if node.op_type == "Conv"]
+        self.assertEqual(len(conv_nodes), 1)
+        self.assertEqual(shape_ranks[conv_nodes[0].input[2]], 1)
+
 
 if __name__ == "__main__":
     unittest.main()