Add comprehensive model card for LEGO (MV-ScanQA, TripAlign)

This PR adds a comprehensive model card for the LEGO model, presented in the paper [Advancing 3D Scene Understanding with MV-ScanQA Multi-View Reasoning Evaluation and TripAlign Pre-training Dataset](https://huggingface.co/papers/2508.11058).

It includes:
- The appropriate `pipeline_tag`: `image-text-to-text`, allowing users to find the model easily on the Hub.
- The `library_name`: `transformers`, indicating compatibility with the 🤗 Transformers library.
- The `license`: `CC-BY-4.0` as specified in the repository.
- Links to the paper, project page, and the official GitHub repository for more detailed information and code.
- An overview of the model and its capabilities based on the paper abstract and GitHub README.
- A sample Python usage example demonstrating how to load and use the LoRA adapter with its Fuyu base model.

Please review and merge this PR if everything looks good.

README.md

@@ -0,0 +1,189 @@
+---
+license: cc-by-4.0
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+# LEGO: A Model for Multi-View 3D Scene Understanding
+
+This repository contains the official weights for **LEGO**, a baseline method for multi-view reasoning in 3D scene understanding. LEGO leverages knowledge from pre-trained 2D LVLMs (specifically fine-tuning a Fuyu-8B model) and is trained using the **TripAlign** pre-training dataset. It is evaluated on **MV-ScanQA**, a novel 3D question answering dataset designed to rigorously test multi-view compositional reasoning.
+
+LEGO achieves state-of-the-art performance on MV-ScanQA, as well as on existing benchmarks for 3D dense captioning and question answering.
+
+This model was presented in the paper [Advancing 3D Scene Understanding with MV-ScanQA Multi-View Reasoning Evaluation and TripAlign Pre-training Dataset](https://huggingface.co/papers/2508.11058).
+
+- 🏠 [Project Page](https://matthewdm0816.github.io/tripalign-mvscanqa)
+- 💻 [GitHub Repository](https://github.com/matthewdm0816/MV-ScanQA-TripAlign)
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/matthewdm0816/MV-ScanQA-TripAlign/main/docs/teasor-mm-lego.svg" alt="LEGO Teaser Image" width="70%"/>
+</div>
+
+## Overview of LEGO, MV-ScanQA, and TripAlign
+
+The **MV-ScanQA** dataset addresses limitations in existing 3D vision-language datasets by introducing questions that explicitly require integrating information from multiple views, thus rigorously testing multi-view compositional reasoning over distant objects.
+
+To facilitate training for such demanding scenarios, the **TripAlign** dataset is introduced. This large-scale, low-cost 2D-3D-language pre-training corpus contains 1M `<2D view, set of 3D objects, text>` triplets, providing richer, view-grounded multi-object multimodal alignment signals than previous single-object annotations.
+
+**LEGO** (Large-scale Multi-View Grounding Objective) is the baseline method developed to tackle the multi-view reasoning challenge in MV-ScanQA. It transfers knowledge from pre-trained 2D LVLMs (like Fuyu-8B, which this model fine-tunes) to the 3D domain with TripAlign.
+
+## Usage
+
+This model is a PEFT (Parameter-Efficient Fine-Tuning) LoRA adapter built on top of the `adept/fuyu-8b` base model. You can load and use it with the `transformers` and `peft` libraries.
+
+First, ensure you have the necessary libraries installed:
+
+```bash
+pip install transformers accelerate peft torch torchvision pillow
+```
+
+Below is a sample code for inference. Please note that the image pre-processing functions (`build_transform`, `find_closest_aspect_ratio`, `dynamic_preprocess`, `load_image`) are adapted from the original repository's usage patterns for Fuyu-based models.
+
+```python
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+def load_image(image_file, input_size=448, max_num=12):
+    image = Image.open(image_file).convert('RGB')
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+# Define the base model and the LoRA adapter ID
+base_model_name_or_path = "adept/fuyu-8b"
+# Replace 'your-org/your-repo' with the actual model ID on Hugging Face Hub
+peft_model_id = "your-org/your-repo" # e.g., kmichiru/LEGO
+
+# Load the base model
+print(f"Loading base model: {base_model_name_or_path}...")
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_name_or_path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True,
+    device_map="auto" # Use 'auto' to load across available devices
+)
+tokenizer = AutoTokenizer.from_pretrained(base_model_name_or_path, trust_remote_code=True, use_fast=False)
+
+# Load the PEFT adapter weights on top of the base model
+print(f"Loading LoRA adapter: {peft_model_id}...")
+model = PeftModel.from_pretrained(base_model, peft_model_id).eval()
+print("Model loaded successfully!")
+
+# Example usage (replace with your image path and question)
+# You might need to download a sample image, e.g., from the GitHub repo
+# A dummy image for testing:
+# from PIL import ImageDraw
+# dummy_image = Image.new('RGB', (800, 600), color = 'red')
+# draw = ImageDraw.Draw(dummy_image)
+# draw.text((10,10), "Sample Image", fill=(0,0,0))
+# dummy_image.save("sample_image.png")
+image_path = "sample_image.png" # Replace with path to a real image
+if not Path(image_path).exists():
+    print(f"Warning: Image '{image_path}' not found. Please provide a valid image path or create a dummy image.")
+    # Exit or handle gracefully if no image is available for execution
+    exit()
+
+pixel_values = load_image(image_path, max_num=6).to(torch.bfloat16).cuda() # Ensure image is on GPU
+generation_config = dict(max_new_tokens=1024, do_sample=True)
+
+question = "Describe the main objects in this 3D scene." # Example question
+# For a Fuyu model, the prompt format might be specific. Refer to Fuyu documentation.
+# This example uses a basic chat format.
+response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
+print(f'User: {question}
+Assistant: {response}')
+
+# Example for 3D question answering (assuming the model outputs bounding box coordinates)
+question_with_bbox = "What is the bounding box of the chair in this scene?"
+response_bbox, history_bbox = model.chat(tokenizer, pixel_values, question_with_bbox, generation_config, history=None, return_history=True)
+print(f'User: {question_with_bbox}
+Assistant: {response_bbox}')
+```
+
+## Citation
+
+If you find this codebase useful, please consider citing our work:
+
+```bibtex
+@inproceedings{mo2025mvscanqa,
+  title={Advancing 3D Scene Understanding with MV-ScanQA Multi-View Reasoning Evaluation and TripAlign Pre-training Dataset},
+  author={Mo, Wentao and Chen, QingChao and Peng, Yuxin and Huang, Siyuan and Liu, Yang},
+  booktitle={Proceedings of the 33rd ACM International Conference on Multimedia},
+  year={2025},
+}
+```
+
+## License
+
+This code repository and datasets are licensed under a [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
+
+Copyright (c) 2025 Wentao Mo.