diff --git a/demo/body3d_pose_lifter_demo.py b/demo/body3d_pose_lifter_demo.py
index 8b834d08ca..02e3014f21 100644
--- a/demo/body3d_pose_lifter_demo.py
+++ b/demo/body3d_pose_lifter_demo.py
@@ -386,7 +386,7 @@ def main():
         pose_results_2d = extract_pose_sequence(
             pose_est_results_list,
             frame_idx=frame_idx,
-            causal=pose_lifter.causal,
+            causal=pose_lift_dataset.get('causal', False),
             seq_len=pose_lift_dataset.get('seq_len', 1),
             step=pose_lift_dataset.get('seq_step', 1))
 
diff --git a/mmpose/apis/inference_3d.py b/mmpose/apis/inference_3d.py
index 2ab81b20a4..f89c33c9ea 100644
--- a/mmpose/apis/inference_3d.py
+++ b/mmpose/apis/inference_3d.py
@@ -180,7 +180,7 @@ def inference_pose_lifter_model(model,
     init_default_scope(model.cfg.get('default_scope', 'mmpose'))
     pipeline = Compose(model.cfg.test_dataloader.dataset.pipeline)
 
-    causal = model.causal
+    causal = model.cfg.test_dataloader.dataset.get('causal', False)
     target_idx = -1 if causal else len(pose_results_2d) // 2
 
     dataset_info = model.dataset_meta
@@ -193,18 +193,21 @@ def inference_pose_lifter_model(model,
             bbox_scale = None
 
     for i, pose_res in enumerate(pose_results_2d):
-        keypoints = []
         for j, data_sample in enumerate(pose_res):
-            keypoint = np.squeeze(data_sample.pred_instances.keypoints, axis=0)
-            if norm_pose_2d:
-                bbox = np.squeeze(data_sample.pred_instances.bboxes)
-                center = np.array([[(bbox[0] + bbox[2]) / 2,
-                                    (bbox[1] + bbox[3]) / 2]])
-                scale = max(bbox[2] - bbox[0], bbox[3] - bbox[1])
-                keypoints.append((keypoint[:, :2] - center) / scale *
-                                 bbox_scale + bbox_center)
-            else:
-                keypoints.append(keypoint[:, :2])
+            kpts = data_sample.pred_instances.keypoints
+            bboxes = data_sample.pred_instances.bboxes
+            keypoints = []
+            for k in range(len(kpts)):
+                kpt = kpts[k]
+                if norm_pose_2d:
+                    bbox = bboxes[k]
+                    center = np.array([[(bbox[0] + bbox[2]) / 2,
+                                        (bbox[1] + bbox[3]) / 2]])
+                    scale = max(bbox[2] - bbox[0], bbox[3] - bbox[1])
+                    keypoints.append((kpt[:, :2] - center) / scale *
+                                     bbox_scale + bbox_center)
+                else:
+                    keypoints.append(kpt[:, :2])
             pose_results_2d[i][j].pred_instances.keypoints = np.array(
                 keypoints)
 
diff --git a/mmpose/visualization/local_visualizer_3d.py b/mmpose/visualization/local_visualizer_3d.py
index 3a0cfc1cb3..8eccd10bf9 100644
--- a/mmpose/visualization/local_visualizer_3d.py
+++ b/mmpose/visualization/local_visualizer_3d.py
@@ -124,7 +124,7 @@ def _draw_3d_data_samples(
 
         plt.ioff()
         fig = plt.figure(
-            figsize=(vis_width * 0.01, vis_height * num_instances * 0.01))
+            figsize=(vis_width * num_instances * 0.01, vis_height * 0.01))
 
         def _draw_3d_instances_kpts(keypoints,
                                     scores,
@@ -135,21 +135,13 @@ def _draw_3d_instances_kpts(keypoints,
             for idx, (kpts, score, visible) in enumerate(
                     zip(keypoints, scores, keypoints_visible)):
 
-                valid = score >= kpt_thr
+                valid = np.logical_and(score >= kpt_thr,
+                                       np.any(~np.isnan(kpts), axis=-1))
 
                 ax = fig.add_subplot(
                     1, num_fig, fig_idx * (idx + 1), projection='3d')
                 ax.view_init(elev=axis_elev, azim=axis_azimuth)
-
-                x_c = np.mean(kpts[valid, 0]) if valid.any() else 0
-                y_c = np.mean(kpts[valid, 1]) if valid.any() else 0
-
-                ax.set_xlim3d([x_c - axis_limit / 2, x_c + axis_limit / 2])
-                ax.set_ylim3d([y_c - axis_limit / 2, y_c + axis_limit / 2])
                 ax.set_zlim3d([0, axis_limit])
-                ax.set_xlabel('x')
-                ax.set_ylabel('y')
-                ax.set_zlabel('z')
                 ax.set_aspect('auto')
                 ax.set_xticks([])
                 ax.set_yticks([])
@@ -157,9 +149,16 @@ def _draw_3d_instances_kpts(keypoints,
                 ax.set_xticklabels([])
                 ax.set_yticklabels([])
                 ax.set_zticklabels([])
+                ax.scatter([0], [0], [0], marker='o', color='red')
+                if title:
+                    ax.set_title(f'{title} ({idx})')
                 ax.dist = axis_dist
 
-                ax.scatter([0], [0], [0], marker='o', color='red')
+                x_c = np.mean(kpts[valid, 0]) if valid.any() else 0
+                y_c = np.mean(kpts[valid, 1]) if valid.any() else 0
+
+                ax.set_xlim3d([x_c - axis_limit / 2, x_c + axis_limit / 2])
+                ax.set_ylim3d([y_c - axis_limit / 2, y_c + axis_limit / 2])
 
                 kpts = np.array(kpts, copy=False)
 
@@ -208,9 +207,6 @@ def _draw_3d_instances_kpts(keypoints,
                             ax.plot(
                                 xs_3d, ys_3d, zs_3d, color=_color, zdir='z')
 
-                if title:
-                    ax.set_title(f'{title} ({idx})')
-
         if 'keypoints' in pred_instances:
             keypoints = pred_instances.get('keypoints',
                                            pred_instances.keypoints)
@@ -254,7 +250,9 @@ def _draw_3d_instances_kpts(keypoints,
         if not pred_img_data.any():
             pred_img_data = np.full((vis_height, vis_width, 3), 255)
         else:
-            pred_img_data = pred_img_data.reshape(vis_height, vis_width, -1)
+            pred_img_data = pred_img_data.reshape(vis_height,
+                                                  vis_width * num_instances,
+                                                  -1)
 
         plt.close(fig)