DreamReward-X: Boosting High-Quality 3D Generation with Human Preference Alignment

Recent advancements in 3D content generation have shown remarkable success by leveraging pretrained large-scale diffusion models. However, existing 3D generation results are far from perfect as one primary challenge lies in aligning 3D content with human preference, especially in text-driven 3D generation. In this paper, we propose a novel 3D generation framework, coined DreamReward, to learn and improve text-driven 3D generation models from human preference feedback. First, we collect 25K+ expert comparisons based on a systematic annotation pipeline including filtering, rating, and ranking. Then, we build Reward3D, the first general-purpose text-to-3D human preference reward model to encode human preferences effectively. Building upon the 3D reward model, we finally perform theoretical analysis and present the Reward3D Feedback Learning (DreamFL) algorithm to guide the noisy pretrained distribution toward the actual user-prompt distributions in optimization. With the rapid development and growing popularity of 4D and image-driven 3D generation, we further extend our DreamReward into 4D generation (DreamReward-4D) and image-to-3D generation (DreamReward-img) in a low-cost but effective manner. Despite the impressive results created by DreamReward, the diversity in text-driven 3D generation is limited due to inherent maximum likelihood-seeking issues. To address this, we explore the gap between Denoising Diffusion Implicit Models (DDIM) and SDS-based DreamFL in the generation process and propose DreamReward++, where we introduce a reward-aware noise sampling strategy to unleash text-driven diversity during the generation process while ensuring human preference alignment. Grounded by theoretical proof and extensive experiment comparisons, our method successfully generates high-fidelity and diverse 3D results with significant boosts in prompt alignment with human intention. Our results demonstrate the great potential for learning from human feedback to improve 3D generation.