Project details

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D2F: AI-Powered Facial Generation from Text

Transforming text into faces using GANs and deep learning.
D2F (Description to Face) is an AI research project where I developed a Generative Adversarial Network (GAN) model that generates hyper-realistic facial images based solely on descriptive text inputs. I led the entire pipeline: from NLP preprocessing and dataset curation to GAN architecture tuning and model training. The project achieved an impressive FID score of 98.45 and was published on SSRN.


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Key Highlights:

  • Built a custom dataset by scraping and preprocessing thousands of text-image pairs.

  • Used TensorFlow and Keras to design and train the GAN.

  • Integrated NLP for converting unstructured descriptions into structured conditioning inputs.

  • Tuned the model to optimize realism and diversity.

  • Published findings in SSRN, gaining traction in AI research and product circles.

Key Highlights:

  • Built a custom dataset by scraping and preprocessing thousands of text-image pairs.

  • Used TensorFlow and Keras to design and train the GAN.

  • Integrated NLP for converting unstructured descriptions into structured conditioning inputs.

  • Tuned the model to optimize realism and diversity.

  • Published findings in SSRN, gaining traction in AI research and product circles.

Key Highlights:

  • Built a custom dataset by scraping and preprocessing thousands of text-image pairs.

  • Used TensorFlow and Keras to design and train the GAN.

  • Integrated NLP for converting unstructured descriptions into structured conditioning inputs.

  • Tuned the model to optimize realism and diversity.

  • Published findings in SSRN, gaining traction in AI research and product circles.

Tech Stack:
TensorFlow, Keras, Python, NLP, GAN, Jupyter, Google Colab

Tech Stack:
TensorFlow, Keras, Python, NLP, GAN, Jupyter, Google Colab

Tech Stack:
TensorFlow, Keras, Python, NLP, GAN, Jupyter, Google Colab

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