Coherent Zero-Shot Visual Instruction Generation
This work addresses the problem of creating consistent multi-step visual instructions for applications in education or tutorials, though it appears incremental as it builds on existing diffusion and LLM models.
The paper tackles the challenge of generating consistent visual instructions across sequential steps using a training-free framework that integrates text comprehension and image generation, achieving coherent and visually pleasing results.
Despite the advances in text-to-image synthesis, particularly with diffusion models, generating visual instructions that require consistent representation and smooth state transitions of objects across sequential steps remains a formidable challenge. This paper introduces a simple, training-free framework to tackle the issues, capitalizing on the advancements in diffusion models and large language models (LLMs). Our approach systematically integrates text comprehension and image generation to ensure visual instructions are visually appealing and maintain consistency and accuracy throughout the instruction sequence. We validate the effectiveness by testing multi-step instructions and comparing the text alignment and consistency with several baselines. Our experiments show that our approach can visualize coherent and visually pleasing instructions