arXiv:2402.07925v1 Announce Type: new
Abstract: Machine learning has enabled the development of powerful systems capable of editing images from natural language instructions. However, in many common scenarios it is difficult for users to specify precise image transformations with text alone. For example, in an image with several dogs, it is difficult to select a particular dog and move it to a precise location. Doing this with text alone would require a complex prompt that disambiguates the target dog and describes the destination. However, direct manipulation is well suited to visual tasks like selecting objects and specifying locations. We introduce Point and Instruct, a system for seamlessly combining familiar direct manipulation and textual instructions to enable precise image manipulation. With our system, a user can visually mark objects and locations, and reference them in textual instructions. This allows users to benefit from both the visual descriptiveness of natural language and the spatial precision of direct manipulation.

Combining Direct Manipulation and Textual Instructions for Precise Image Manipulation

Machine learning has made significant advancements in image editing from natural language instructions. However, one common challenge users face is specifying precise image transformations using text alone. This is particularly difficult when dealing with complex scenes, such as images with multiple similar objects.

In the case of an image with several dogs, for example, it can be challenging to select a specific dog and move it to an exact location using text alone. This would require a complex prompt that distinguishes the target dog and describes the destination in great detail. However, direct manipulation, a technique commonly used in visual tasks, is better suited for selecting objects and specifying locations with precision.

The authors introduce Point and Instruct, a system that seamlessly combines direct manipulation and textual instructions for precise image manipulation. With this system, users can visually mark objects and locations and reference them in textual instructions. This approach allows users to leverage the descriptive power of natural language along with the spatial precision of direct manipulation.

Point and Instruct brings together concepts from multiple disciplines, bridging the gap between natural language processing, computer vision, and human-computer interaction. By integrating these fields, the system offers a more intuitive and effective way for users to communicate their desired image edits.

This research holds promise for applications in graphic design, content creation, and image-based data analysis. By providing users with a versatile tool that combines direct manipulation and textual instructions, it becomes easier to iterate and experiment with visual designs. Moreover, this approach could enhance the accessibility of image editing tools for individuals with limited text-based communication abilities.

The multi-disciplinary nature of Point and Instruct highlights the importance of collaboration and cross-pollination between different fields. By combining expertise from machine learning, computer vision, natural language processing, and human-computer interaction, we can develop more powerful and user-friendly systems. As research continues to advance in these areas, we can expect even more sophisticated and precise image editing tools to be developed in the future.

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