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.

Motivation

The current paper focuses on technical aspects of reward design but does not explore the integration of human feedback, which could provide more nuanced and context-sensitive guidance for model training. Human-in-the-loop methodologies could enhance the model's ability to generate more realistic and contextually appropriate 3D objects.

Proposed Method

Conduct experiments where human evaluators provide feedback on generated 3D models, which is then used to adjust the reward function dynamically. Implement this feedback loop in the RL training pipeline and compare the quality of models generated with and without human feedback using existing benchmarks.

Expected Contribution

This approach could lead to more refined 3D models and provide insights into integrating qualitative human judgment into AI model training, improving both the fidelity and applicability of generated content.

Required Resources

Human participants for feedback, computational resources for training, and expertise in RL and human-computer interaction.

Source Paper

Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation

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