diff --git a/lmms_eval/tasks/spatialviz/_default_template_yaml b/lmms_eval/tasks/spatialviz/_default_template_yaml
new file mode 100644
index 000000000..d59d3e396
--- /dev/null
+++ b/lmms_eval/tasks/spatialviz/_default_template_yaml
@@ -0,0 +1,28 @@
+dataset_path: PLM-Team/Spatial-Visualization-Benchmark
+
+generation_kwargs:
+  max_new_tokens: 8192
+  temperature: 0.0
+  top_p: 1.0
+  num_beams: 1
+  do_sample: false
+
+output_type: generate_until
+doc_to_visual: !function utils.spatialviz_doc_to_visual
+doc_to_text: !function utils.spatialviz_doc_to_text
+doc_to_target: utils.spatialviz_doc_to_target
+process_results: !function utils.spatialviz_process_results
+
+metric_list:
+  - metric: spatialviz_score
+    aggregation: !function utils.spatialviz_aggregate_results
+    higher_is_better: true
+
+dataset_kwargs:
+  token: True
+  cache_dir: SpatialViz
+  force_download: true
+
+# metadata:
+#   strategy: CoT # ['Direct', 'CoT']
+#   use_lmms_judge: False
\ No newline at end of file
diff --git a/lmms_eval/tasks/spatialviz/spatialviz.yaml b/lmms_eval/tasks/spatialviz/spatialviz.yaml
new file mode 100644
index 000000000..4ad96ee07
--- /dev/null
+++ b/lmms_eval/tasks/spatialviz/spatialviz.yaml
@@ -0,0 +1,5 @@
+# Official SpatialViz paper use this configuration
+dataset_name: 
+test_split: test
+task: "spatialviz_full"
+include: _default_template_yaml
diff --git a/lmms_eval/tasks/spatialviz/utils.py b/lmms_eval/tasks/spatialviz/utils.py
new file mode 100644
index 000000000..fa248f451
--- /dev/null
+++ b/lmms_eval/tasks/spatialviz/utils.py
@@ -0,0 +1,145 @@
+import os
+import re
+from collections import defaultdict
+from pathlib import Path
+
+import yaml
+from huggingface_hub import snapshot_download
+from PIL import Image
+
+with open(Path(__file__).parent / "_default_template_yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+    config = yaml.safe_load("".join(safe_data))
+
+cache_dir = snapshot_download(
+    repo_id=config["dataset_path"],
+    repo_type="dataset",
+    local_dir_use_symlinks=False,
+)
+
+
+def spatialviz_doc_to_visual(doc):
+    visual = []
+
+    category = doc["Category"]
+    task = doc["Task"]
+    level = doc["Level"]
+    image_id = doc["Image_id"]
+    image_path = f"{cache_dir}/{category}/{task}/{level}/{image_id}.png"
+
+    if os.path.exists(image_path):
+        image_path = image_path
+        visual.append(Image.open(image_path).convert("RGB"))
+    else:
+        raise FileExistsError(f"video path:{image_path} does not exist.")
+    return visual
+
+
+def spatialviz_doc_to_text(doc):
+    ops = ["A", "B", "C", "D"]
+    prompt = "You should first provide a reasoning process, then provide a single option(A, B, C or D) as the final answer. The reasoning process and the answer are enclosed within <think></think> and <answer></answer> tags, respectively, i.e., <think>reasoning process</think>, <answer>answer</answer>.\n"
+    question = doc["Question"]
+    choices = doc["Choices"]
+    choice_text = ""
+
+    for i, choice in enumerate(choices):
+        choice_text += ops[i] + ". " + choice + "\n"
+    text = prompt + "Question: " + question + "\n" + choice_text
+    return text
+
+
+def spatialviz_process_results(doc, results):
+    key_name = "spatialviz_score"
+    grounded_output = doc["Answer"]
+    response = results[0]
+
+    think_pattern = r"<think>(.*?)</think>"
+    answer_pattern = r"<answer>(.*?)</answer>"
+
+    think_match = re.search(think_pattern, response, re.DOTALL)
+    answer_match = re.search(answer_pattern, response, re.DOTALL)
+    if think_match and answer_match:
+        final_answer = answer_match.group(1).strip()
+        pred_answer = final_answer.split(".")[0]
+        op = re.findall(r"[A-D]", pred_answer)
+
+    else:
+        print("No match for think/answer \n")
+        final_answer_patterns = ["<answer>", "Answer:", "Final answer", "final answer", "Final Answer", "the answer is", "The answer is", "correct answer", "Correct answer", "Correct Answer", "答案" "correct path"]
+        if len(response) == 1:
+            op = re.findall(r"[A-D]", response)
+        else:
+            for pattern in final_answer_patterns:
+                if pattern in response:
+                    response = response.split(pattern)[-1].strip()
+                    op = re.findall(r"[A-D]", response.split(".")[0])
+                    break
+
+    op = list(set(op))
+
+    if len(op) == 1 and grounded_output == op[0].upper():
+        is_correct = True
+    else:
+        is_correct = False
+
+    query = spatialviz_doc_to_text(doc)
+    spatialviz_submission = {"id": doc["Image_id"], "query": query, "gt_content": grounded_output, "pred": response, "category": doc["Category"], "task": doc["Task"], "level": doc["Level"], "is_correct": is_correct}
+    return {key_name: spatialviz_submission}
+
+
+def spatialviz_aggregate_results(results):
+    task_to_eval_samples = defaultdict(list)
+    category_to_eval_samples = defaultdict(list)
+    key_to_eval_samples = defaultdict(list)
+    total_samples = len(results)
+    total_correct = 0
+
+    for sample in results:
+        task = sample["task"]
+        category = sample["category"]
+        level = sample["level"]
+        key = f"{category}-{task}-{level}"
+        is_correct = sample["is_correct"]
+
+        if is_correct:
+            total_correct += 1
+            task_to_eval_samples[task].append(1)
+            category_to_eval_samples[category].append(1)
+            key_to_eval_samples[key].append(1)
+        else:
+            task_to_eval_samples[task].append(0)
+            category_to_eval_samples[category].append(0)
+            key_to_eval_samples[key].append(0)
+
+    accuracy = total_correct / total_samples if total_samples > 0 else 0
+    task_accuracies = {task: sum(scores) / len(scores) for task, scores in task_to_eval_samples.items()}
+    category_accuracies = {category: sum(scores) / len(scores) for category, scores in category_to_eval_samples.items()}
+    key_accuracies = {key: sum(scores) / len(scores) for key, scores in key_to_eval_samples.items()}
+    print(f"{'Total Samples':<20}: {total_samples}")
+    print(f"{'Total Correct':<20}: {total_correct}")
+    print(f"{'Overall Accuracy':<20}: {accuracy:.4f}")
+    print()
+
+    print(f"{'Per-Task Accuracy':<40}")
+    print("-" * 40)
+    for task, acc in task_accuracies.items():
+        print(f"{task:<20}: {acc:.4f}")
+    print()
+
+    print(f"{'Per-Category Accuracy':<40}")
+    print("-" * 40)
+    for category, acc in category_accuracies.items():
+        print(f"{category:<20}: {acc:.4f}")
+    print("=" * 40)
+
+    print(f"{'Per-Key Accuracy':<40}")
+    print("-" * 40)
+    for key, acc in key_accuracies.items():
+        print(f"{key:<20}: {acc:.4f}")
+    print()
+    return accuracy