diff --git a/README.md b/README.md
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+---
+license: cc-by-nc-4.0
+task_categories:
+- image-segmentation
+- object-detection
+- depth-estimation
+tags:
+- computer-vision
+- synthetic-data
+- robotics
+- autonomous-driving
+- amr
+- slam
+- blender
+- procedural-generation
+pretty_name: DataFurnace AMR Synthetic Dataset
+size_categories:
+- n<1K
+---
+
+# 🏭 DataFurnace: AMR & SLAM Synthetic Evaluation Dataset
+
+![DataFurnace Showcase](./assets/Warehouse_Zapping_Promo_0001_NORMAL.gif)
+*Fully synchronized multi-pass rendering (RGB, Depth, Semantic Mask, and 3D Bounding Box).*
+
+---
+
+## 📌 Overview
+
+**DataFurnace** is a procedurally generated synthetic dataset designed for evaluating and training:
+
+- Autonomous Mobile Robots (AMR)  
+- Automated Guided Vehicles (AGV)  
+- SLAM / VIO / 3D Perception algorithms  
+- Warehouse robotics navigation systems  
+
+All ground truth is generated directly from the underlying 3D scene graph, ensuring **deterministic pixel-level and millimeter-level accuracy** across all modalities.  
+**No generative AI is used**, eliminating copyright, privacy, and hallucination concerns.
+
+---
+
+## 🚀 Key Features
+
+### 🔹 1. Perfectly Synchronized Multi-Pass Rendering
+Each frame contains fully aligned multi-modal outputs:
+
+- **RGB** — Physically lit color images  
+- **Depth** — True Z-depth with LiDAR noise simulation
+- **Semantic Mask** — Pixel-perfect class segmentation (Floor, Rack, Box, Pallet)
+- **3D Bounding Box (JSON)** — 6DoF absolute coordinates and dimensions
+- **Costmap** — 2D top-down occupancy grid (generated per scene/mode)
+
+### 🔹 2. Extreme Hazard Simulation for Robustness Testing
+To stress-test SLAM and perception systems, four lighting conditions are provided:
+
+- **NORMAL** — Standard warehouse lighting
+- **BROKEN** — Random darkened areas / flickering lights
+- **GLARE** — Volumetric scattering, lens flare, white-out
+- **EDGE_CASE** — Foreground blackout + background overexposure
+
+---
+
+## 📂 Dataset Structure
+
+This repository contains curated sample sequences generated by the DataFurnace pipeline.
+
+- **5 Unique Warehouse Environments**
+- **Layout Variations** (Normal / Anomaly with Scattered Obstacles)
+- **4 Lighting Conditions** per environment
+- Sequential Frames per sequence (from robot cameras: Front/Back/Left/Right)
+
+### Directory Layout (Example)
+```text
+dataset/
+  └── Warehouse_Scene_0001/
+      └── normal/
+          └── NORMAL/
+              ├── Warehouse_Scene_0001_NORMAL_Cam_Front_F001_Normal_RGB.png
+              ├── Warehouse_Scene_0001_NORMAL_Cam_Front_F001_Normal_Depth.png
+              ├── Warehouse_Scene_0001_NORMAL_Cam_Front_F001_Normal_Mask.png
+              ├── Warehouse_Scene_0001_NORMAL_Cam_Front_F001_Normal_BBox.json
+              └── ...
+🧠 3D Bounding Box (JSON) Format
+The dataset provides absolute 3D spatial data (6DoF), not just 2D projection. Camera poses and objects' Volumetric Centers are perfectly recorded.
+
+JSON
+{
+    "frame": 1,
+    "lighting_mode": "BROKEN",
+    "camera_name": "Cam_Front",
+    "camera_pose": {
+        "location": {"x": 0.0, "y": -10.0, "z": 0.4},
+        "rotation": {"x": 1.5708, "y": 0.0, "z": 0.0}
+    },
+    "objects": [
+        {
+            "class": "Box",
+            "name": "ANOMALY_CardboardBox.248",
+            "location": [-0.3007, 1.0295, 0.132],
+            "rotation_euler": [0.0001, 0.0, -1.8494],
+            "dimensions": [0.4266, 0.3861, 0.2639]
+        }
+    ]
+}
+📥 How to Use (Hugging Face Datasets)
+Python
+from datasets import load_dataset
+
+# Load the dataset (Example usage)
+ds = load_dataset("jp-cypress/DataFurnace-AMR")
+
+🛠️ Verification Tools
+Utility Python scripts are included in the scripts/ directory to help you visualize the ground truth accuracy without affecting the raw data.
+
+Bash
+# 1. Visualize 3D Bounding Boxes mathematically projected onto RGB images
+python scripts/draw_bbox_overlay.py --input ./dataset/Warehouse_Scene_0001/normal/NORMAL
+
+# 2. Generate a zapping GIF to easily review multi-modal alignment
+python scripts/generate_promo_gif.py --input ./dataset/... --overlay ./output_bbox --out final.gif
+📜 License
+This dataset is released under CC BY-NC 4.0.
+
+Commercial use is not permitted without explicit permission.
+
+🌐 Citation
+If you use DataFurnace in academic or industrial research, please cite this repository:
+
+@dataset{datafurnace2026,
+  author = {2.5D Asset Factory},
+  title = {DataFurnace: AMR & SLAM Synthetic Evaluation Dataset},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/jp-cypress/DataFurnace-AMR}
+}
\ No newline at end of file
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+++ b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F017_Normal_RGB.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d8aa5121fb0a7d984913e1005fc46a13644e402ade0b2446c4b17053e5ba3f6
+size 737215
diff --git a/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F018_Normal_BBox.json b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F018_Normal_BBox.json
new file mode 100644
index 0000000000000000000000000000000000000000..6b6a85f4002a7a17fadc1935b42e2975928fe8da
--- /dev/null
+++ b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F018_Normal_BBox.json
@@ -0,0 +1,18 @@
+{
+    "frame": 18,
+    "lighting_mode": "NORMAL",
+    "camera_name": "Cam_Front",
+    "camera_pose": {
+        "location": {
+            "x": 0.0,
+            "y": 7.8947,
+            "z": 0.4
+        },
+        "rotation": {
+            "x": 1.5708,
+            "y": -0.0,
+            "z": 0.0
+        }
+    },
+    "objects": []
+}
\ No newline at end of file
diff --git a/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F018_Normal_Depth.png b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F018_Normal_Depth.png
new file mode 100644
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--- /dev/null
+++ b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F018_Normal_Depth.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 168634
diff --git a/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F018_Normal_Mask.png b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F018_Normal_Mask.png
new file mode 100644
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+++ b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F018_Normal_Mask.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F018_Normal_RGB.png b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F018_Normal_RGB.png
new file mode 100644
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--- /dev/null
+++ b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F018_Normal_RGB.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aabf83b32734b4e081682e601e7ffb83c29f9461f56bbd08bdd5e87fb0c8fc86
+size 718702
diff --git a/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F019_Normal_BBox.json b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F019_Normal_BBox.json
new file mode 100644
index 0000000000000000000000000000000000000000..8890f0996b47d4e17f053038e9c9004540a7ae7e
--- /dev/null
+++ b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F019_Normal_BBox.json
@@ -0,0 +1,18 @@
+{
+    "frame": 19,
+    "lighting_mode": "NORMAL",
+    "camera_name": "Cam_Front",
+    "camera_pose": {
+        "location": {
+            "x": 0.0,
+            "y": 8.9474,
+            "z": 0.4
+        },
+        "rotation": {
+            "x": 1.5708,
+            "y": -0.0,
+            "z": 0.0
+        }
+    },
+    "objects": []
+}
\ No newline at end of file
diff --git a/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F019_Normal_Depth.png b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F019_Normal_Depth.png
new file mode 100644
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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new file mode 100644
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F019_Normal_RGB.png b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F019_Normal_RGB.png
new file mode 100644
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--- /dev/null
+++ b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F019_Normal_RGB.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e97ef6452fb5eac607acd620c06c1960e1f740c63993ffaefb7db3ec915cb333
+size 625184
diff --git a/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F020_Normal_BBox.json b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F020_Normal_BBox.json
new file mode 100644
index 0000000000000000000000000000000000000000..19465fc084df8857f11cea0d9f40316231ec60eb
--- /dev/null
+++ b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F020_Normal_BBox.json
@@ -0,0 +1,18 @@
+{
+    "frame": 20,
+    "lighting_mode": "NORMAL",
+    "camera_name": "Cam_Front",
+    "camera_pose": {
+        "location": {
+            "x": 0.0,
+            "y": 10.0,
+            "z": 0.4
+        },
+        "rotation": {
+            "x": 1.5708,
+            "y": -0.0,
+            "z": 0.0
+        }
+    },
+    "objects": []
+}
\ No newline at end of file
diff --git a/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F020_Normal_Depth.png b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F020_Normal_Depth.png
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+version https://git-lfs.github.com/spec/v1
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diff --git a/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F020_Normal_Mask.png b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F020_Normal_Mask.png
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F020_Normal_RGB.png b/dataset/Warehouse_Scene_0005/normal/NORMAL/Warehouse_Scene_0005_NORMAL_Cam_Front_F020_Normal_RGB.png
new file mode 100644
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2cc02e1dd6bb6d5de14e81c9edee101f5af8a159ec24396df1e4529c7960c2ee
+size 531805
diff --git a/scripts/draw_bbox_overlay.py b/scripts/draw_bbox_overlay.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3c30dcdfb569a54754e2dceb8ab4a9fc7848047
--- /dev/null
+++ b/scripts/draw_bbox_overlay.py
@@ -0,0 +1,191 @@
+"""
+Constructive Furnace: 3D Bounding Box Visualization Tool
+(空間認識AI・SLAM評価用 3Dバウンディングボックス可視化ツール)
+
+This script reads a procedural synthetic dataset (RGB images and JSON metadata) 
+and projects physically accurate 3D bounding boxes onto the 2D image plane.
+
+Usage:
+    1. Place this script in the same directory as the dataset files.
+    2. Run: python draw_bbox_overlay.py --input ./ --output ./output_bbox
+
+Dependencies:
+    pip install opencv-python numpy
+"""
+
+import os
+import glob
+import json
+import sys
+import argparse
+
+try:
+    import cv2
+    import numpy as np
+except ImportError:
+    print(" [Error] Missing required packages: pip install opencv-python numpy")
+    sys.exit(1)
+
+# ==========================================
+# 設定（Settings）
+# ==========================================
+# キャリブレーション済みのカメラ内部パラメータ
+FOCAL_LENGTH_MM = 18.0  
+SENSOR_WIDTH_MM = 36.0
+
+COLOR_MAP = {
+    "Box": (0, 165, 255),       
+    "Pallet": (0, 255, 0),      
+    "Rack": (255, 0, 0),        
+    "Unknown": (0, 0, 255),
+    "DEFAULT": (0, 0, 255)      
+}
+
+# 描画用パラメータ
+THICKNESS = 2
+FONT = cv2.FONT_HERSHEY_SIMPLEX
+FONT_SCALE = 0.5
+FONT_THICKNESS = 1
+
+# ==========================================
+# 3D 座標変換・数学ロジック（安定版）
+# ==========================================
+def get_euler_matrix(rx, ry, rz):
+    cx, sx = np.cos(rx), np.sin(rx)
+    cy, sy = np.cos(ry), np.sin(ry)
+    cz, sz = np.cos(rz), np.sin(rz)
+    
+    Rx = np.array([[1, 0, 0], [0, cx, -sx], [0, sx, cx]])
+    Ry = np.array([[cy, 0, sy], [0, 1, 0], [-sy, 0, cy]])
+    Rz = np.array([[cz, -sz, 0], [sz, cz, 0], [0, 0, 1]])
+    
+    return Rz @ Ry @ Rx
+
+def project_3d_points(local_corners, obj_pose, cam_pose, img_w, img_h):
+    # ===============================================
+    # 🌟 オリジナル版の堅牢なベクトル演算ロジックを復元
+    # ===============================================
+    
+    # オブジェクトの回転と位置
+    rx, ry, rz = obj_pose["rotation_euler"]
+    R_obj = get_euler_matrix(rx, ry, rz)
+    T_obj = np.array(obj_pose["location"])
+    
+    # 8頂点を一括でワールド座標へ変換
+    world_pts = (R_obj @ local_corners.T).T + T_obj
+    
+    # カメラの回転と位置
+    R_cam = get_euler_matrix(cam_pose["rotation"]["x"], cam_pose["rotation"]["y"], cam_pose["rotation"]["z"])
+    T_cam = np.array([cam_pose["location"]["x"], cam_pose["location"]["y"], cam_pose["location"]["z"]])
+    
+    # 一括でカメラローカル座標へ変換 (R_cam.T は逆行列と等価)
+    cam_local_pts = (R_cam.T @ (world_pts - T_cam).T).T
+    
+    # Blender -> OpenCV の座標系変換 (Y, Zの符号を反転)
+    pts_cv = np.zeros_like(cam_local_pts)
+    pts_cv[:, 0] = cam_local_pts[:, 0]   
+    pts_cv[:, 1] = -cam_local_pts[:, 1]  
+    pts_cv[:, 2] = -cam_local_pts[:, 2]  
+    
+    # カメラ内部パラメータによる透視投影
+    f_pixels = img_w * (FOCAL_LENGTH_MM / SENSOR_WIDTH_MM)
+    cx, cy = img_w / 2.0, img_h / 2.0
+    
+    pts_2d = []
+    for x, y, z in pts_cv:
+        # カメラの背後（Z<=0.1）にある頂点は破綻を防ぐためクリッピング
+        if z <= 0.1: 
+            return None
+            
+        u = int((x / z) * f_pixels + cx)
+        v = int((y / z) * f_pixels + cy)
+        pts_2d.append((u, v))
+        
+    return pts_2d
+
+# ==========================================
+# メイン処理（汎用化バッチ）
+# ==========================================
+def main():
+    parser = argparse.ArgumentParser(description="Draw 3D Bounding Boxes on RGB images.")
+    parser.add_argument("--input", "-i", default="./", help="Directory containing RGB images and JSON metadata")
+    parser.add_argument("--output", "-o", default="./output_bbox", help="Directory to save output images")
+    args = parser.parse_args()
+
+    input_dir = args.input
+    output_dir = args.output
+
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    search_pattern = os.path.join(input_dir, "*_RGB.png")
+    rgb_files = glob.glob(search_pattern)
+
+    if not rgb_files:
+        print(f" [Warn] No RGB images found in {input_dir}. Please check the path.")
+        return
+
+    print(f" [Info] Found {len(rgb_files)} images. Starting BBox projection...")
+
+    for rgb_path in rgb_files:
+        base_name = rgb_path.replace("_RGB.png", "")
+        json_path = f"{base_name}_BBox.json"
+        
+        if not os.path.exists(json_path):
+            print(f" [Skip] JSON not found for {os.path.basename(rgb_path)}")
+            continue
+
+        with open(json_path, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+            
+        img = cv2.imread(rgb_path)
+        if img is None:
+            continue
+            
+        img_h, img_w = img.shape[:2]
+        cam_pose = data.get("camera_pose", data.get("camera"))
+        objects = data.get("objects", [])
+        
+        for obj in objects:
+            # 1. クラス名の取得と色の割り当て
+            class_name = obj.get("class", "DEFAULT")
+            color = COLOR_MAP.get(class_name, COLOR_MAP["DEFAULT"])
+            
+            # 2. リスト形式 [w, d, h] として取得する
+            w, d, h = obj["dimensions"]
+            
+            # 3. オフセット無し、純粋な中心(0,0,0)から広がる8頂点
+            x_min, x_max = -w/2, w/2
+            y_min, y_max = -d/2, d/2
+            z_min, z_max = -h/2, h/2
+            
+            local_corners = np.array([
+                [x_min, y_min, z_min], [x_max, y_min, z_min], [x_max, y_max, z_min], [x_min, y_max, z_min], 
+                [x_min, y_min, z_max], [x_max, y_min, z_max], [x_max, y_max, z_max], [x_min, y_max, z_max]  
+            ])
+            
+            pts_2d = project_3d_points(local_corners, obj, cam_pose, img_w, img_h)
+            if pts_2d is None:
+                continue 
+                
+            # ワイヤーフレームの描画
+            edges = [(0, 1), (1, 2), (2, 3), (3, 0), (4, 5), (5, 6), (6, 7), (7, 4), (0, 4), (1, 5), (2, 6), (3, 7)]
+            for start, end in edges:
+                cv2.line(img, pts_2d[start], pts_2d[end], color, THICKNESS)
+                
+            # クラス名のテキストラベル描画
+            min_x = min([p[0] for p in pts_2d])
+            min_y = min([p[1] for p in pts_2d])
+            label_text = f"{class_name}"
+            (tw, th), baseline = cv2.getTextSize(label_text, FONT, FONT_SCALE, FONT_THICKNESS)
+            
+            cv2.rectangle(img, (min_x, min_y - th - 5), (min_x + tw, min_y), color, -1)
+            cv2.putText(img, label_text, (min_x, min_y - 5), FONT, FONT_SCALE, (255, 255, 255), FONT_THICKNESS)
+
+        out_filename = os.path.basename(rgb_path).replace("_RGB.png", "_BBox_Overlay.png")
+        out_path = os.path.join(output_dir, out_filename)
+        cv2.imwrite(out_path, img)
+        print(f" [OK] Generated -> {out_filename}")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/scripts/generate_promo_gif.py b/scripts/generate_promo_gif.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2693a83fc6d6c8383bff8617ecfb78d6053752f
--- /dev/null
+++ b/scripts/generate_promo_gif.py
@@ -0,0 +1,95 @@
+import os
+import glob
+import cv2
+import numpy as np
+import imageio.v2 as imageio
+import argparse
+import sys
+
+# ==========================================
+# 設定（デフォルト値）
+# ==========================================
+DEFAULT_FPS = 15
+DEFAULT_RESIZE_SCALE = 0.5 
+
+def main():
+    parser = argparse.ArgumentParser(description="倉庫シミュレーションプロモーション用GIF生成ツール")
+    
+    # 💡 required=True を外し、デフォルト値を設定（引数なしでも動くようにする）
+    parser.add_argument("--input", default="./", help="RGB/Mask/Depth画像が含まれる入力ディレクトリ（デフォルト: ./）")
+    parser.add_argument("--overlay", default="./", help="BBox重畳画像が含まれるディレクトリ（デフォルト: ./）")
+    parser.add_argument("--output", default="Warehouse_Promo.gif", help="出力するGIFファイル名")
+    parser.add_argument("--fps", type=int, default=DEFAULT_FPS, help="GIFのフレームレート")
+    parser.add_argument("--scale", type=float, default=DEFAULT_RESIZE_SCALE, help="リサイズ倍率 (0.0~1.0)")
+    
+    # 引数を解析（引数がなくても、上記の default 値が args に格納される）
+    args = parser.parse_args()
+
+    print("🎬 プロモーション用ザッピングGIFの生成を開始します...")
+    
+    # 1. FrontカメラのRGB画像を基準にリストを取得
+    rgb_files = sorted(glob.glob(os.path.join(args.input, "*_Cam_Front_F*_RGB.png")))
+    
+    if not rgb_files:
+        print(f"❌ RGB画像が見つかりません。パスを確認してください: {os.path.abspath(args.input)}")
+        print("💡 ヒント: コマンドライン引数でパスを指定するか、このスクリプトを画像と同じフォルダに置いて実行してください。")
+        print("例: python generate_promo_gif.py --input ./dataset --overlay ./bbox")
+        return
+
+    total_frames = len(rgb_files)
+    print(f"📦 総フレーム数: {total_frames}枚 を検出しました。")
+    
+    phase_1_end = int(total_frames * 0.20)
+    phase_2_end = int(total_frames * 0.35)
+    phase_3_end = int(total_frames * 0.50)
+    
+    gif_frames = []
+    
+    for i, rgb_path in enumerate(rgb_files):
+        base_name = os.path.basename(rgb_path).replace("_RGB.png", "")
+        
+        # フェーズに応じた読み込みパスを決定
+        if i < phase_1_end:
+            target_path = rgb_path
+            phase_name = "1. RGB (Raw CG)"
+        elif i < phase_2_end:
+            target_path = os.path.join(args.input, f"{base_name}_Mask.png")
+            phase_name = "2. Semantic Mask"
+        elif i < phase_3_end:
+            target_path = os.path.join(args.input, f"{base_name}_Depth.png")
+            phase_name = "3. Depth (LiDAR)"
+        else:
+            target_path = os.path.join(args.overlay, f"{base_name}_BBox_Overlay.png")
+            phase_name = "4. 3D Bounding Box (Ground Truth)"
+
+        if not os.path.exists(target_path):
+            print(f"⚠️ 警告: ファイル未発見: {target_path} (RGBで代用)")
+            target_path = rgb_path
+            
+        nparr = np.fromfile(target_path, np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        
+        if img is None:
+            continue
+
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        
+        # リサイズ処理
+        if args.scale != 1.0:
+            h, w = img.shape[:2]
+            img = cv2.resize(img, (int(w * args.scale), int(h * args.scale)), interpolation=cv2.INTER_AREA)
+
+        # フェーズ名の描画
+        cv2.putText(img, phase_name, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2, cv2.LINE_AA)
+        
+        gif_frames.append(img)
+        print(f"  [{i+1}/{total_frames}] {phase_name} 処理中...")
+
+    # 2. GIF動画として書き出し
+    print(f"💾 GIFファイルを保存しています: {args.output}")
+    imageio.mimsave(args.output, gif_frames, fps=args.fps, loop=0)
+    
+    print(f"✨ 完了！ {args.output} が生成されました。")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file