Conceitos essenciais
Learning from human feedback to improve text-to-3D models.
Resumo
The content discusses the DreamReward framework for text-to-3D generation, focusing on human preference alignment. It introduces Reward3D and DreamFL algorithms to optimize 3D models based on human feedback. The paper outlines the process of constructing a 3D dataset, training the Reward3D model, and implementing DreamFL for high-fidelity text-to-3D generation aligned with human preferences. Extensive experiments and comparisons with baselines demonstrate the effectiveness of DreamReward in generating quality 3D assets.
Introduction
Significance of 3D content generation.
Advancements in diffusion models for automated 3D generation.
Related Work
Evolution of text-to-image and text-to-3D generation methods.
Overall Framework
Introduction to DreamReward framework for human preference alignment.
Reward3D
Annotation pipeline design for prompt selection and 3D collection.
Training details of the Reward3D model using ImageReward as a backbone.
DreamFL
Explanation of Score Distillation Sampling theory.
Implementation details of DreamFL algorithm for optimizing 3D results.
Experiments
Comparative experiments on DreamReward against baseline models.
User studies evaluating alignment, quality, and consistency scores.
Estatísticas
"We collect 25k expert comparisons based on a systematic annotation pipeline."
"Our results demonstrate significant boosts in prompt alignment with human intention."
"Training Reward3D on a single GPU (24GB) with specific optimization parameters."
Citações
"RLHF uses human feedback to enhance generative model performance."
"DreamReward aligns closely with given prompts while maintaining visual consistency."