text-to-pose

A model for generating poses from text and further generating images.

CommonProductImageText-to-imagePose estimation

text-to-pose is a research project aimed at generating character poses from text descriptions and using these poses to create images. This technology combines natural language processing and computer vision, achieving text-to-image generation by enhancing the control and quality of diffusion models. The project is based on a paper published at the NeurIPS 2024 Workshop, showcasing innovation and cutting-edge advancements. The key advantages of this technology include improved accuracy and controllability in image generation, as well as potential applications in artistic creation and virtual reality.

Text-to-pose conversion: Utilizing Transformer architecture to convert text descriptions into character poses.
Pose-to-image generation: Generating high-quality images based on the generated poses using diffusion models.
Model training and optimization: Providing training code and pre-trained models for easy use by researchers and developers.
Dataset creation: Offering datasets for training and testing
including the COCO-2017 annotated dataset.
Model comparison: Demonstrating poses and images generated using different models for easy effect comparison.
Code and documentation: Providing detailed code and documentation to facilitate user understanding and usage.

The target audience primarily includes researchers
developers
and artists in the fields of computer vision and natural language processing. Researchers can explore the mechanisms of text-to-image generation using this technology
developers can create new applications leveraging this technology
and artists can utilize it to produce innovative artworks.

Researchers use the text-to-pose model to generate character poses and images based on specific textual descriptions for behavioral analysis studies.
Game developers apply this technology to create NPC character poses and images in games
enhancing the realism of the gameplay.
Artists leverage this technology to create artworks based on text descriptions
exploring new forms of artistic expression.

1. Visit the GitHub project page and clone or download the code.
2. Read the README file to understand the project structure and dependencies.
3. Install the required libraries and environment.
4. Follow the documentation to run the code for model training or testing.
5. Use the provided interface to input textual descriptions and generate corresponding character poses.

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