  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.
