example of creating meshes

shap_e/examples/sample_text_to_3d.ipynb

@@ -80,6 +80,21 @@
     "    images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)\n",
     "    display(gif_widget(images))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85a4dce4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example of saving the latents as meshes.\n",
+    "from shap_e.util.notebooks import decode_latent_mesh\n",
+    "\n",
+    "for i, latent in enumerate(latents):\n",
+    "    with open(f'example_mesh_{i}.ply', 'wb') as f:\n",
+    "        decode_latent_mesh(xm, latent).tri_mesh().write_ply(f)"
+   ]
   }
  ],
  "metadata": {

shap_e/models/stf/renderer.py

@@ -256,6 +256,16 @@ def render_views_from_stf(
     if output_srgb:
         tf_out.channels = _convert_srgb_to_linear(tf_out.channels)
 
+    # Make sure the raw meshes have colors.
+    with torch.autocast(device_type, enabled=False):
+        textures = tf_out.channels.float()
+        assert len(textures.shape) == 3 and textures.shape[-1] == len(
+            texture_channels
+        ), f"expected [meta_batch x inner_batch x texture_channels] field results, but got {textures.shape}"
+        for m, texture in zip(raw_meshes, textures):
+            texture = texture[: len(m.verts)]
+            m.vertex_channels = {name: ch for name, ch in zip(texture_channels, texture.unbind(-1))}
+
     args = dict(
         options=options,
         texture_channels=texture_channels,
@@ -315,6 +325,8 @@ def _render_with_pytorch3d(
     raw_meshes: List[TorchMesh],
     tf_out: AttrDict,
 ):
+    _ = tf_out
+
     # Lazy import because pytorch3d is installed lazily.
     from shap_e.rendering.pytorch3d_util import (
         blender_uniform_lights,
@@ -328,14 +340,6 @@ def _render_with_pytorch3d(
     device_type = device.type
 
     with torch.autocast(device_type, enabled=False):
-        textures = tf_out.channels.float()
-        assert len(textures.shape) == 3 and textures.shape[-1] == len(
-            texture_channels
-        ), f"expected [meta_batch x inner_batch x texture_channels] field results, but got {textures.shape}"
-        for m, texture in zip(raw_meshes, textures):
-            texture = texture[: len(m.verts)]
-            m.vertex_channels = {name: ch for name, ch in zip(texture_channels, texture.unbind(-1))}
-
         meshes = convert_meshes(raw_meshes)
 
         lights = blender_uniform_lights(

shap_e/util/notebooks.py

@@ -9,6 +9,7 @@ from PIL import Image
 
 from shap_e.models.nn.camera import DifferentiableCameraBatch, DifferentiableProjectiveCamera
 from shap_e.models.transmitter.base import Transmitter, VectorDecoder
+from shap_e.rendering.torch_mesh import TorchMesh
 from shap_e.util.collections import AttrDict
 
 
@@ -60,6 +61,21 @@ def decode_latent_images(
     return [Image.fromarray(x) for x in arr]
 
 
+@torch.no_grad()
+def decode_latent_mesh(
+    xm: Union[Transmitter, VectorDecoder],
+    latent: torch.Tensor,
+) -> TorchMesh:
+    decoded = xm.renderer.render_views(
+        AttrDict(cameras=create_pan_cameras(2, latent.device)),  # lowest resolution possible
+        params=(xm.encoder if isinstance(xm, Transmitter) else xm).bottleneck_to_params(
+            latent[None]
+        ),
+        options=AttrDict(rendering_mode="stf", render_with_direction=False),
+    )
+    return decoded.raw_meshes[0]
+
+
 def gif_widget(images):
     writer = io.BytesIO()
     images[0].save(