diff --git a/docs/utils/datatypes.md b/docs/utils/datatypes.md
new file mode 100644
index 00000000..5e0560bd
--- /dev/null
+++ b/docs/utils/datatypes.md
@@ -0,0 +1,12 @@
+---
+comments: true
+status: new
+---
+
+# Data Types Utils
+
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.datatypes.keypoints_to_detections">keypoints_to_detections</a></h2>
+</div>
+
+:::supervision.utils.datatypes.keypoints_to_detections
diff --git a/mkdocs.yml b/mkdocs.yml
index b30dbcfc..8c939cf0 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -79,6 +79,7 @@ nav:
           - File: utils/file.md
           - Draw: utils/draw.md
           - Geometry: utils/geometry.md
+          - Datatypes: utils/datatypes.md
       - Assets: assets.md
   - Cookbooks: cookbooks.md
   - Cheatsheet: https://roboflow.github.io/cheatsheet-supervision/
diff --git a/supervision/__init__.py b/supervision/__init__.py
index 746b2f67..04813ef2 100644
--- a/supervision/__init__.py
+++ b/supervision/__init__.py
@@ -100,6 +100,7 @@ from supervision.keypoint.core import KeyPoints
 from supervision.metrics.detection import ConfusionMatrix, MeanAveragePrecision
 from supervision.tracker.byte_tracker.core import ByteTrack
 from supervision.utils.conversion import cv2_to_pillow, pillow_to_cv2
+from supervision.utils.datatypes import keypoints_to_detections
 from supervision.utils.file import list_files_with_extensions
 from supervision.utils.image import (
     ImageSink,
diff --git a/supervision/utils/datatypes.py b/supervision/utils/datatypes.py
new file mode 100644
index 00000000..dafbc4cb
--- /dev/null
+++ b/supervision/utils/datatypes.py
@@ -0,0 +1,65 @@
+from typing import Iterable, Optional
+
+import numpy as np
+
+from supervision.detection.core import Detections
+from supervision.keypoint.core import KeyPoints
+
+
+def keypoints_to_detections(
+    keypoints: KeyPoints, selected_keypoint_indices: Optional[Iterable[int]] = None
+) -> Detections:
+    """
+    Convert a KeyPoints object to a Detections object. This
+    approximates the bounding box of the detected object by
+    taking the bounding box that fits all keypoints.
+
+    Arguments:
+        keypoints (KeyPoints): The keypoints to convert to detections.
+        selected_keypoint_indices (Optional[Iterable[int]]): The
+            indices of the keypoints to include in the bounding box
+            calculation. This helps focus on a subset of keypoints,
+            e.g. when some are occluded. Captures all keypoints by default.
+
+    Returns:
+        detections (Detections): The converted detections object.
+
+    Example:
+        ```python
+        keypoints = sv.KeyPoints.from_inference(...)
+        detections = keypoints_to_detections(keypoints)
+        ```
+    """
+    if keypoints.is_empty():
+        return Detections.empty()
+
+    detections_list = []
+    for i, xy in enumerate(keypoints.xy):
+        if selected_keypoint_indices:
+            xy = xy[selected_keypoint_indices]
+        x_min = xy[:, 0].min()
+        x_max = xy[:, 0].max()
+        y_min = xy[:, 1].min()
+        y_max = xy[:, 1].max()
+        xyxy = np.array([[x_min, y_min, x_max, y_max]], dtype=np.float32)
+
+        if keypoints.confidence is None:
+            confidence = None
+        else:
+            confidence = keypoints.confidence[i]
+            if selected_keypoint_indices:
+                confidence = confidence[selected_keypoint_indices]
+            confidence = np.array([confidence.mean()], dtype=np.float32)
+
+        detections_list.append(
+            Detections(
+                xyxy=xyxy,
+                confidence=confidence,
+            )
+        )
+
+    detections = Detections.merge(detections_list)
+    detections.class_id = keypoints.class_id
+    detections.data = keypoints.data
+
+    return detections