ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions (2412.01987v2)

Abstract: The goal of this work is to generate step-by-step visual instructions in the form of a sequence of images, given an input image that provides the scene context and the sequence of textual instructions. This is a challenging problem as it requires generating multi-step image sequences to achieve a complex goal while being grounded in a specific environment. Part of the challenge stems from the lack of large-scale training data for this problem. The contribution of this work is thus three-fold. First, we introduce an automatic approach for collecting large step-by-step visual instruction training data from instructional videos. We apply this approach to one million videos and create a large-scale, high-quality dataset of 0.6M sequences of image-text pairs. Second, we develop and train ShowHowTo, a video diffusion model capable of generating step-by-step visual instructions consistent with the provided input image. Third, we evaluate the generated image sequences across three dimensions of accuracy (step, scene, and task) and show our model achieves state-of-the-art results on all of them. Our code, dataset, and trained models are publicly available.

• The paper introduces a large-scale dataset of 0.6 million image-text pairs and a diffusion model for generating context-aware visual instructions.
• It employs spatial and temporal attention mechanisms to ensure each generated frame aligns with both the textual prompts and the initial scene context.
• The framework achieves state-of-the-art performance in step accuracy, scene consistency, and task faithfulness, enabling practical applications in assistive technologies.

Generating Scene-Conditioned Step-by-Step Visual Instructions: An Analysis of the "ShowHowTo" Framework

The paper "ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions" presents an innovative approach to generating visual instructions from textual descriptions in conjunction with an input image that provides contextual scene information. This work explores the complex task of producing ordered image sequences that adhere to a sequence of textual instructions while maintaining consistency with an input image's context. The authors address the significant challenges posed by the absence of extensive datasets for this specific task and the technical intricacies of generating coherent and contextually grounded visual sequences.

Key Contributions

The paper's contributions are threefold:

1. Dataset Creation: The paper introduces a novel method for automatically collecting extensive step-by-step visual instruction datasets from publicly available instructional videos. The approach has been implemented on a scale, resulting in a substantial dataset comprising 0.6 million sequences of image-text pairs. This achievement is particularly noteworthy given the scarcity of manually annotated data suitable for training models in this domain.
2. Model Development: The authors have developed ShowHowTo, a video diffusion model designed to generate step-by-step visual instructions. The model is trained using the large-scale dataset mentioned above and is tailored to produce image sequences that are consistent with a specified starting scene provided by the user through an input image. The model effectively employs textual conditioning at each step to ensure that the generated sequence accurately corresponds to the provided instructions.
3. Evaluation of Model Performance: Through rigorous evaluations across three dimensions—step accuracy, scene consistency, and task faithfulness—the ShowHowTo model is demonstrated to achieve state-of-the-art results. Notably, the model shows proficiency in maintaining scene integrity and producing semantically correct instructional steps.

Technical Insights

The technical accomplishments of the ShowHowTo framework are underpinned by a diffusion model that integrates both spatial and temporal attention mechanisms. The model’s architecture is strategically designed to manage the input image as a conditional baseline while utilizing sequential text prompts corresponding to each instructional step. This dual conditioning is pivotal in ensuring that each frame aligns not only with the textual description but also fits cohesively with the established scene context.

Training a model of this complexity requires meticulous handling of sequence lengths to enable variable-length instruction generation while maintaining computational efficiency. This is achieved by sampling data sequences during training across various batch sizes, ensuring that the model adapts to varying task lengths without performance degradation.

Implications and Future Directions

The implications of this research extend beyond theoretical advancements; the practical applications are far-reaching. The capability to generate task-specific visual instructions that integrate seamlessly with a user's environment opens new possibilities in personalized assistive technologies, educational tools, and robotic guidance systems. The ShowHowTo framework can be instrumental in developing intelligent assistants that provide contextually relevant visual instructions, significantly enhancing user experience in interactive and automated systems.

Furthermore, the paper lays a robust foundation for future research aimed at refining visual instruction generation. The methodology for dataset creation is invaluable and can be adapted for broader applications, covering a wider range of tasks beyond those demonstrated. The modular nature of the model architecture suggests potential extensions to incorporate more sophisticated scene understanding and more nuanced interaction modalities, such as voice-controlled or gesture-based commands.

Conclusion

The ShowHowTo framework represents a significant stride towards bridging the gap between textual instructions and visual task execution. By utilizing cutting-edge video diffusion models and creating comprehensive datasets from unstructured video content, this work elevates the field of scene-conditioned visual instruction generation. It sets a benchmark for future explorations in AI-generated visual guidance, promising to enhance both theoretical understandings and practical implementations in AI systems.