REVISION: Rendering Tools Enable Spatial Fidelity in Vision-Language Models

📃 Paper | 🎮 Project Website

📄 Abstract

Text-to-Image (T2I) and multimodal large language models (MLLMs) have been adopted in solutions for several computer vision and multimodal learning tasks. However, it has been found that such vision-language models lack the ability to correctly reason over spatial relationships. To tackle this shortcoming, we develop the REVISION framework which improves spatial fidelity in vision-language models. REVISION is a 3D rendering based pipeline that generates spatially accurate synthetic images, given a textual prompt. REVISION is an extendable framework, which currently supports 100+ 3D assets, 11 spatial relationships, all with diverse camera perspectives and backgrounds. Leveraging images from REVISION as additional guidance in a training-free manner consistently improves the spatial consistency of T2I models across all spatial relationships, achieving competitive performance on the VISOR and T2I-CompBench benchmarks. We also design RevQA, a question-answering benchmark to evaluate the spatial reasoning abilities of MLLMs, and find that state-of-the-art models are not robust to complex spatial reasoning under adversarial settings. Our results and findings indicate that utilizing rendering-based frameworks is an effective approach for developing spatially-aware generative models.

📚 Contents

• Data
• Sample Scripts
• Citing
• Acknowledgments

🖼️ Data

Please refer to the REVISION organization on Hugging Face 🤗.

📊 Sample Scripts

inference.py presents a simple script that can be modified based on the input prompt and a REVISION image.

📜 Citing

@misc{chatterjee2024revisionrenderingtoolsenable,
      title={REVISION: Rendering Tools Enable Spatial Fidelity in Vision-Language Models}, 
      author={Agneet Chatterjee and Yiran Luo and Tejas Gokhale and Yezhou Yang and Chitta Baral},
      year={2024},
      eprint={2408.02231},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2408.02231}, 
}

🙏 Acknowledgments

The authors acknowledge resources and support from the Research Computing facilities at Arizona State University. This work was supported by NSF RI grants #1750082 and #2132724. The views and opinions of the authors expressed herein do not necessarily state or reflect those of the funding agencies and employers.