diff --git a/io_scene_psk_psa/__init__.py b/io_scene_psk_psa/__init__.py
index 7570fe9..4b2589f 100644
--- a/io_scene_psk_psa/__init__.py
+++ b/io_scene_psk_psa/__init__.py
@@ -34,6 +34,7 @@ if 'bpy' in locals():
     importlib.reload(psa_reader)
     importlib.reload(psa_writer)
     importlib.reload(psa_builder)
+    importlib.reload(psa_importer)
     importlib.reload(psa_export_properties)
     importlib.reload(psa_export_operators)
     importlib.reload(psa_export_ui)
diff --git a/io_scene_psk_psa/psa/importer.py b/io_scene_psk_psa/psa/importer.py
index d2f5a18..aad193f 100644
--- a/io_scene_psk_psa/psa/importer.py
+++ b/io_scene_psk_psa/psa/importer.py
@@ -1,4 +1,3 @@
-import math
 import typing
 from typing import List, Optional
 
@@ -81,7 +80,7 @@ def _get_armature_bone_index_for_psa_bone(psa_bone_name: str, armature_bone_name
     return None
 
 
-def _resample_sequence_data_matrix(sequence_data_matrix: np.ndarray, target_frame_count: int):
+def _resample_sequence_data_matrix(sequence_data_matrix: np.ndarray, time_step: float = 1.0) -> np.ndarray:
     '''
     Resamples the sequence data matrix to the target frame count.
     @param sequence_data_matrix: FxBx7 matrix where F is the number of frames, B is the number of bones, and X is the
@@ -89,38 +88,40 @@ def _resample_sequence_data_matrix(sequence_data_matrix: np.ndarray, target_fram
     @param target_frame_count: The number of frames to resample to.
     @return: The resampled sequence data matrix, or sequence_data_matrix if no resampling is necessary.
     '''
-    def get_sample_times(source_frame_count: int, target_frame_count: int) -> typing.Iterable[float]:
+    def get_sample_times(source_frame_count: int, time_step: float) -> typing.Iterable[float]:
+        # TODO: for correctness, we should also emit the target frame time as well (because the last frame can be a
+        #  fractional frame).
         time = 0.0
-        time_step = source_frame_count / target_frame_count
-        while time <= target_frame_count - 1:
+        while time < source_frame_count - 1:
             yield time
             time += time_step
-        yield 1.0
+        yield source_frame_count - 1
 
-    source_frame_count, bone_count = sequence_data_matrix.shape[:1]
-
-    if target_frame_count == source_frame_count:
+    if time_step == 1.0:
         # No resampling is necessary.
         return sequence_data_matrix
 
+    source_frame_count, bone_count = sequence_data_matrix.shape[:2]
+    sample_times = list(get_sample_times(source_frame_count, time_step))
+    target_frame_count = len(sample_times)
     resampled_sequence_data_matrix = np.zeros((target_frame_count, bone_count, 7), dtype=float)
-    sample_times = get_sample_times(source_frame_count, target_frame_count)
 
-    for frame_index, sample_time in enumerate(sample_times):
+    for sample_index, sample_time in enumerate(sample_times):
+        frame_index = int(sample_time)
         if sample_time % 1.0 == 0.0:
             # Sample time has no fractional part, so just copy the frame.
-            resampled_sequence_data_matrix[frame_index, :, :] = sequence_data_matrix[int(sample_time), :, :]
+            resampled_sequence_data_matrix[sample_index, :, :] = sequence_data_matrix[frame_index, :, :]
         else:
             # Sample time has a fractional part, so interpolate between two frames.
+            next_frame_index = frame_index + 1
             for bone_index in range(bone_count):
-                frame_index = int(sample_time)
                 source_frame_1_data = sequence_data_matrix[frame_index, bone_index, :]
-                source_frame_2_data = sequence_data_matrix[frame_index + 1, bone_index, :]
+                source_frame_2_data = sequence_data_matrix[next_frame_index, bone_index, :]
                 factor = sample_time - frame_index
                 q = Quaternion((source_frame_1_data[:4])).slerp(Quaternion((source_frame_2_data[:4])), factor)
                 q.normalize()
                 l = Vector(source_frame_1_data[4:]).lerp(Vector(source_frame_2_data[4:]), factor)
-                resampled_sequence_data_matrix[frame_index, bone_index, :] = q.w, q.x, q.y, q.z, l.x, l.y, l.z
+                resampled_sequence_data_matrix[sample_index, bone_index, :] = q.w, q.x, q.y, q.z, l.x, l.y, l.z
 
     return resampled_sequence_data_matrix
 
@@ -268,18 +269,16 @@ def import_psa(context: Context, psa_reader: PsaReader, armature_object: Object,
                     sequence_data_matrix[frame_index, bone_index] = _calculate_fcurve_data(import_bone, key_data)
 
             # Resample the sequence data to the target FPS.
-            # TODO: target frame count can be fractional.
-            target_frame_count = math.ceil(sequence.frame_count * (target_fps / sequence.fps))
+            # If the target frame count is the same as the source frame count, this will be a no-op.
+            resampled_sequence_data_matrix = _resample_sequence_data_matrix(sequence_data_matrix,
+                                                                            time_step=sequence.fps / target_fps)
 
             # Write the keyframes out.
             # Note that the f-curve data consists of alternating time and value data.
+            target_frame_count = resampled_sequence_data_matrix.shape[0]
             fcurve_data = np.zeros(2 * target_frame_count, dtype=float)
             fcurve_data[0::2] = range(0, target_frame_count)
 
-            # Resample the sequence data matrix to the target frame count.
-            # If the target frame count is the same as the source frame count, this will be a no-op.
-            resampled_sequence_data_matrix = _resample_sequence_data_matrix(sequence_data_matrix, target_frame_count)
-
             for bone_index, import_bone in enumerate(import_bones):
                 if import_bone is None:
                     continue