diff --git a/trellis/renderers/mesh_renderer.py b/trellis/renderers/mesh_renderer.py
index b504fa4d..085f1565 100644
--- a/trellis/renderers/mesh_renderer.py
+++ b/trellis/renderers/mesh_renderer.py
@@ -57,7 +57,7 @@ def render(
             mesh : MeshExtractResult,
             extrinsics: torch.Tensor,
             intrinsics: torch.Tensor,
-            return_types = ["mask", "normal", "depth"]
+            return_types = ["mask", "normal", "depth", "color"]
         ) -> edict:
         """
         Render the mesh.
diff --git a/trellis/utils/postprocessing_utils.py b/trellis/utils/postprocessing_utils.py
index 0a8d9fb7..3840b0f8 100644
--- a/trellis/utils/postprocessing_utils.py
+++ b/trellis/utils/postprocessing_utils.py
@@ -397,7 +397,7 @@ def tv_loss(texture):
 
 
 def to_glb(
-    app_rep: Union[Strivec, Gaussian],
+    app_rep: Union[Strivec, Gaussian, MeshExtractResult],
     mesh: MeshExtractResult,
     simplify: float = 0.95,
     fill_holes: bool = True,
@@ -410,7 +410,7 @@ def to_glb(
     Convert a generated asset to a glb file.
 
     Args:
-        app_rep (Union[Strivec, Gaussian]): Appearance representation.
+        app_rep (Union[Strivec, Gaussian, MeshExtractResult]): Appearance representation.
         mesh (MeshExtractResult): Extracted mesh.
         simplify (float): Ratio of faces to remove in simplification.
         fill_holes (bool): Whether to fill holes in the mesh.
diff --git a/trellis/utils/render_utils.py b/trellis/utils/render_utils.py
index 8187c84f..46b0a2fd 100644
--- a/trellis/utils/render_utils.py
+++ b/trellis/utils/render_utils.py
@@ -82,7 +82,9 @@ def render_frames(sample, extrinsics, intrinsics, options={}, colors_overwrite=N
                 rets['depth'].append(None)
         else:
             res = renderer.render(sample, extr, intr)
+            if 'color' not in rets: rets['color'] = []
             if 'normal' not in rets: rets['normal'] = []
+            rets['color'].append(np.clip(res['color'].detach().cpu().numpy().transpose(1, 2, 0) * 255, 0, 255).astype(np.uint8))
             rets['normal'].append(np.clip(res['normal'].detach().cpu().numpy().transpose(1, 2, 0) * 255, 0, 255).astype(np.uint8))
     return rets