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deruyter92 · Mar 20, 2026 · Feb 25, 2026 · Feb 25, 2026 · Feb 25, 2026 · Feb 25, 2026
commit dd042b41e2cc24094743c99ae597deeb1d3b01de
diff --git a/deeplabcut/modelzoo/models_to_framework.json b/deeplabcut/modelzoo/models_to_framework.json
@@ -5,5 +5,5 @@
     "rtmpose_s": "pytorch",
     "rtmpose_x": "pytorch",
     "fmpose3d_humans": "pytorch",
-    "fmpose3d_animals": "pytorch"
+    "fmpose3d_animals": "pytorch",
 }
diff --git a/deeplabcut/pose_estimation_pytorch/modelzoo/fmpose_3d/__init__.py b/deeplabcut/pose_estimation_pytorch/modelzoo/fmpose_3d/__init__.py
@@ -7,7 +7,7 @@
 Licensed under GNU Lesser General Public License v3.0
 """
 
-# NOTE: this module may contain items that need refactoring during 
+# NOTE: this module may contain items that need refactoring during
 # the keypoint migration.
 
 # kpt_refactor - Needs attention when refactoring keypoints

diff --git a/deeplabcut/pose_estimation_pytorch/modelzoo/fmpose_3d/inference.py b/deeplabcut/pose_estimation_pytorch/modelzoo/fmpose_3d/inference.py
@@ -48,8 +48,7 @@ def _pose2d_to_dlc_predictions(
     all_scores = np.asarray(pose_2d.scores)
     if all_kpts.ndim != 4 or all_scores.ndim != 3:
         raise ValueError(
-            "Expected pose_2d keypoints/scores shaped as "
-            "(num_persons, num_frames, num_bodyparts, {2 or score})."
+            "Expected pose_2d keypoints/scores shaped as (num_persons, num_frames, num_bodyparts, {2 or score})."
         )
 
     num_frames = all_kpts.shape[1]
@@ -68,9 +67,7 @@ def _pose2d_to_dlc_predictions(
 #  i/o migration to validated keypoint schemas (parquet)
 def _poses3d_to_dataframe(poses_3d: list[np.ndarray], df_2d, scorer_3d: str):
     """Create and fill a 3D dataframe using the shared auxiliary helper."""
-    df_3d, scorer_3d, bodyparts = auxiliaryfunctions_3d.create_empty_df(
-        df_2d, scorer_3d, "3d"
-    )
+    df_3d, scorer_3d, bodyparts = auxiliaryfunctions_3d.create_empty_df(df_2d, scorer_3d, "3d")
     n_frames = len(poses_3d)
     n_bodyparts = len(bodyparts)
     arr = np.full((n_frames, n_bodyparts, 3), np.nan, dtype=float)
@@ -117,7 +114,7 @@ def _video_inference_fmpose3d(
         )
     max_individuals = 1
 
-    if device is None or device == 'auto':
+    if device is None or device == "auto":
         device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
     if isinstance(video_paths, (str, Path)):
@@ -136,9 +133,7 @@ def _video_inference_fmpose3d(
 
     api = get_fmpose3d_inference_api(model_type=model_name, device=device)
 
-    dest_folder = (
-        Path(video_paths[0]).parent if dest_folder is None else Path(dest_folder)
-    )
+    dest_folder = Path(video_paths[0]).parent if dest_folder is None else Path(dest_folder)
     dest_folder.mkdir(parents=True, exist_ok=True)
 
     if create_labeled_video:
@@ -163,8 +158,7 @@ def _process_batch(frames: list[np.ndarray]) -> None:
             num_detected = int(np.asarray(pose_2d.keypoints).shape[0])
             if num_detected > 1 and not warned_multi_person_2d:
                 logger.warning(
-                    "Multiple 2D detections (%s) were found, but FMPose3D 3D lifting "
-                    "uses only the first individual.",
+                    "Multiple 2D detections (%s) were found, but FMPose3D 3D lifting uses only the first individual.",
                     num_detected,
                 )
                 warned_multi_person_2d = True
@@ -185,7 +179,6 @@ def _process_batch(frames: list[np.ndarray]) -> None:
                 logger.info("Skipping 3D lifting for batch due to invalid 2D result: %s", e)
                 all_poses_3d.extend([np.zeros((0, num_bodyparts, 3)) for _ in frames])
 
-
         batch: list[np.ndarray] = []
         for frame in tqdm(video, desc="FMPose3D inference"):
             batch.append(frame)
@@ -225,10 +218,7 @@ def _process_batch(frames: list[np.ndarray]) -> None:
         with open(output_json, "w") as f:
             json.dump(predictions_2d, f, cls=NumpyEncoder)
 
-        poses_3d_serialisable = [
-            pose.tolist() if isinstance(pose, np.ndarray) else pose
-            for pose in all_poses_3d
-        ]
+        poses_3d_serialisable = [pose.tolist() if isinstance(pose, np.ndarray) else pose for pose in all_poses_3d]
         output_3d_json = dest_folder / f"{output_prefix}_3d.json"
         with open(output_3d_json, "w") as f:
             json.dump(