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GenHowTo: Learning to Generate Actions and State Transformations from Instructional Videos (2312.07322v2)

Published 12 Dec 2023 in cs.CV

Abstract: We address the task of generating temporally consistent and physically plausible images of actions and object state transformations. Given an input image and a text prompt describing the targeted transformation, our generated images preserve the environment and transform objects in the initial image. Our contributions are threefold. First, we leverage a large body of instructional videos and automatically mine a dataset of triplets of consecutive frames corresponding to initial object states, actions, and resulting object transformations. Second, equipped with this data, we develop and train a conditioned diffusion model dubbed GenHowTo. Third, we evaluate GenHowTo on a variety of objects and actions and show superior performance compared to existing methods. In particular, we introduce a quantitative evaluation where GenHowTo achieves 88% and 74% on seen and unseen interaction categories, respectively, outperforming prior work by a large margin.

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Citations (11)
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Summary

  • The paper introduces an AI model that leverages instructional video triplets to generate temporally plausible, context-consistent action transformations.
  • It employs a dual technique combining image captioning with dataset conditioning to maintain scene fidelity while integrating dynamic modifications.
  • Evaluated with metrics like FID and accuracy, GenHowTo outperforms existing methods and opens new possibilities in robotics, video editing, and gaming.

Understanding GenHowTo: Innovative AI for Video-Based Image Generation

Introduction to GenHowTo

In a breakthrough for computer vision, GenHowTo emerges as an advanced AI model designed to transform static images into dynamic scenes depicting actions or changes in object states. Unlike typical models that may struggle to incorporate context or sustain environmental consistency, GenHowTo stands out by generating temporally and physically plausible images that respect the original setting.

Dataset and Model Development

Key to GenHowTo's success is its unique training process. The model has been equipped with an extensive collection of image triplets from instructional videos, specifically targeting three critical states: the initial state, the action, and the eventual transformation. By capitalizing on image captioning technology, it assigns precise text prompts to describe the targeted transformations, which guide the image generation process.

The conditioning of GenHowTo on this substantial dataset is a result of a two-pronged technique. The model learns to maintain the unchanged aspects of a scene, such as the environment, while adeptly introducing new elements or modifying existing ones as per the instruction provided in the text prompt. Moreover, it leverages a dataset that has been automatically mined from a vast array of online instructional videos, providing a rich and varied learning experience.

Evaluation and Results

GenHowTo's performance excels when it comes to maintaining scene integrity while generating new states of objects within those scenes. It's been rigorously evaluated using quantitative metrics like accuracy and Fréchet Inception Distance (FID), which measure the resemblance between generated images and actual frames depicting the action or final object state. These evaluations reveal that GenHowTo significantly outperforms existing methods, particularly in its capacity to preserve the consistency of the environment from the source image.

Application and Impact

The capabilities of GenHowTo are expansive. In robotics, it offers the potential for creating intermediate goals for machines to accomplish visual tasks. For video editing and game development, it can synthesize actions and transformations aligned with user-defined narratives while preserving scene fidelity.

However, the technology isn't without limitations. Though powerful, GenHowTo can falter with rapid movements or ill-represented objects in the training data, leading to inconsistencies or inaccuracies. These are acknowledged as areas for further refinement.

The societal implications of GenHowTo raise important considerations. As with all AI advancements, ethical use and bias scrutiny are paramount to ensure its benefits are maximized without inadvertently introducing or perpetuating societal issues.

Conclusion

GenHowTo represents a significant advancement in the field of AI-driven image transformation. By intelligently generating action and transformation visuals that seamlessly blend with their original backdrops, it demonstrates a stride toward more intuitive and realistic computer vision applications. While the technology continues to evolve, its current success opens up numerous possibilities for practical applications and further innovation in visual AI.

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  1. GenHowTo: Learning to Generate Actions and State Transformations from Instructional Videos
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