Add pipeline tag and sample usage

Hi! I'm Niels from the Hugging Face community team. This PR improves your model card by adding the `image-to-3d` pipeline tag to the metadata, which helps with model discoverability on the Hub. I've also added a "Sample Usage" section with a code snippet showing how to download the checkpoints using the `huggingface_hub` library, based on the instructions in your GitHub README.

README.md

@@ -1,5 +1,4 @@
 ---
-license: apache-2.0
 datasets:
 - multicam
 - stereo4d
@@ -10,14 +9,17 @@ datasets:
 - point_odyssey
 - re10k
 - dl3dv
+license: apache-2.0
 metrics:
 - psnr
 - ssim
 - lpips
+pipeline_tag: image-to-3d
 ---
+
 # Model Card for Fast Spatial Memory Models
 
-This repo is a public release of [**Fast Spatial Memory with Elastic Test-Time Training**](https://mars-tin.github.io/blogs/posts/elastic_ttt.html), as well as a *self-retrained (non-official!)* version of [**4D-LRM**](https://4dlrm.github.io/).
+This repo is a public release of [**Fast Spatial Memory with Elastic Test-Time Training**](https://fast-spatial-memory.github.io/), as well as a *self-retrained (non-official!)* version of [**4D-LRM**](https://4dlrm.github.io/).
 
 ## Model Details
 
@@ -25,12 +27,39 @@ This repo is a public release of [**Fast Spatial Memory with Elastic Test-Time T
 
 - **Developed by:** [MIT-IBM Watson Lab]
 - **License:** [Apache License 2.0]
+- **Task:** 3D/4D Reconstruction from long observation sequences.
+
+### Model Sources
+
+- **Repository:** [https://github.com/Mars-tin/fast-spatial-mem](https://github.com/Mars-tin/fast-spatial-mem)
+- **Paper:** [https://arxiv.org/abs/2604.07350](https://arxiv.org/abs/2604.07350)
+- **Homepage:** [https://fast-spatial-memory.github.io/](https://fast-spatial-memory.github.io/)
 
-### Model Sources [optional]
+## Sample Usage
 
-- **Repository:** [https://github.com/Mars-tin/fast-spatial-mem]
-- **Paper:** [https://arxiv.org/abs/2604.07350]
-- **Homepage:** [https://fast-spatial-memory.github.io/]
+You can download the pretrained weights from this repository using the `hf_hub_download` function from the `huggingface_hub` library:
+
+```python
+import os
+import shutil
+from huggingface_hub import hf_hub_download
+
+repo_id = "marstin/fast-spatial-mem"
+local_path = "static/weights"
+path_in_repo = "lvsm_checkpoints/fsm_4dlvsm_patch8_res256.pth"
+
+# Download (cached under ~/.cache/huggingface/hub)
+cached_path = hf_hub_download(
+    repo_id=repo_id,
+    filename=path_in_repo,
+    repo_type="model"
+)
+
+# Copy to your desired local folder
+os.makedirs(os.path.dirname(local_path), exist_ok=True)
+target_path = os.path.join(local_path, os.path.basename(path_in_repo))
+shutil.copy(cached_path, target_path)
+```
 
 ## Performance Documentations