Text-to-3D RL with Human Feedback

# Text-to-3D RL with Human Feedback ## Description This project aims to incorporate human feedback into the reward function of a Reinforcement Learning (RL) based system for text-to-3D model generation. The hypothesis is that human feedback can significantly improve the quality and realism of the generated 3D models. ## Research Hypothesis Incorporating human feedback into the reward function of RL-based text-to-3D generation can significantly improve the quality and realism of the generated 3D models. ## Implementation Approach The approach involves: 1. Developing an RL training pipeline for text-to-3D generation. 2. Integrating human feedback into the reward function. 3. Comparing the quality of models generated with and without human feedback. ## Setup Instructions 1. Install Python 3.8+ 2. Install required dependencies using `pip install -r requirements.txt` 3. Ensure access to a GPU for training. ## Usage Examples Run the training script: ```bash python src/train.py ``` Evaluate the model: ```bash python src/evaluate.py ``` ## Expected Results The project aims to produce more realistic and contextually appropriate 3D models by integrating human feedback. ## References - [Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation](http://arxiv.org/abs/2512.10949v1)