Summary of Shapewords: Guiding Text-to-image Synthesis with 3d Shape-aware Prompts, by Dmitry Petrov et al.
ShapeWords: Guiding Text-to-Image Synthesis with 3D Shape-Aware Prompts
by Dmitry Petrov, Pradyumn Goyal, Divyansh Shivashok, Yuanming Tao, Melinos Averkiou, Evangelos Kalogerakis
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary ShapeWords is an innovative approach to synthesizing images by combining 3D shape guidance with text prompts. This method embeds target 3D shape information within specialized tokens alongside input text, effectively integrating 3D shape awareness with textual context to guide the image synthesis process. Unlike traditional methods relying on depth maps from fixed viewpoints, ShapeWords generates diverse yet consistent images that reflect both the target shape’s geometry and textual description. The experimental results demonstrate that ShapeWords produces images that are more compliant with text prompts, aesthetically plausible, while maintaining 3D shape awareness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ShapeWords is a new way to create images by combining words and shapes. It takes a text prompt and a 3D shape as input, then creates an image that looks like the shape and follows the text description. This is different from other methods that only use depth maps or pictures from one angle. ShapeWords makes more realistic and diverse images that fit with the text and show the shape correctly. |
Keywords
» Artificial intelligence » Image synthesis » Prompt
