diff --git a/dlclive/pose_estimation_pytorch/runner.py b/dlclive/pose_estimation_pytorch/runner.py
index 11188f1..551640c 100644
--- a/dlclive/pose_estimation_pytorch/runner.py
+++ b/dlclive/pose_estimation_pytorch/runner.py
@@ -13,6 +13,7 @@
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Literal
+import warnings
 
 import numpy as np
 import torch
@@ -131,15 +132,25 @@ def __init__(
         path: str | Path,
         device: str = "auto",
         precision: Literal["FP16", "FP32"] = "FP32",
-        single_animal: bool = True,
+        single_animal: bool | None = None,
         dynamic: dict | dynamic_cropping.DynamicCropper | None = None,
         top_down_config: dict | TopDownConfig | None = None,
     ) -> None:
         super().__init__(path)
         self.device = _parse_device(device)
         self.precision = precision
+        if single_animal is not None:
+            warnings.warn(
+                "The `single_animal` parameter is deprecated and will be removed "
+                "in a future version. The number of individuals will be automaticalliy inferred "
+                "from the model configuration. Remove argument `single_animal` or set "
+                "`single_animal=None` to accept the inferred value and silence this warning.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
         self.single_animal = single_animal
-
+        self.n_individuals = None
+        self.n_bodyparts = None
         self.cfg = None
         self.detector = None
         self.model = None
@@ -191,9 +202,14 @@ def get_pose(self, frame: np.ndarray) -> np.ndarray:
 
             frame_batch, offsets_and_scales = self._prepare_top_down(tensor, detections)
             if len(frame_batch) == 0:
-                offsets_and_scales = [(0, 0), 1]
-            else:
-                tensor = frame_batch  # still CHW, batched
+                zero_pose = (
+                    np.zeros((self.n_bodyparts, 3)) 
+                    if self.n_individuals < 2 else 
+                    np.zeros((self.n_individuals, self.n_bodyparts, 3))
+                )
+                return zero_pose
+            
+            tensor = frame_batch  # still CHW, batched
 
         if self.dynamic is not None:
             tensor = self.dynamic.crop(tensor)
@@ -260,6 +276,15 @@ def load_model(self) -> None:
         raw_data = torch.load(self.path, map_location="cpu", weights_only=True)
 
         self.cfg = raw_data["config"]
+        
+        # Infer single animal mode and n_bodyparts from model configuration
+        individuals = self.cfg.get("metadata", {}).get("individuals", ['idv1'])
+        bodyparts = self.cfg.get("metadata", {}).get("bodyparts", [])
+        self.n_individuals = len(individuals)
+        self.n_bodyparts = len(bodyparts)
+        if self.single_animal is None:
+            self.single_animal = self.n_individuals == 1
+
         self.model = models.PoseModel.build(self.cfg["model"])
         self.model.load_state_dict(raw_data["pose"])
         self.model = self.model.to(self.device)