解决3D生成中纹理与形状的权衡难题，提升真实感与几何精度。
Target-Balanced Score Distillation
- 将生成建模为多目标优化，自适应平衡正负提示影响。
- 在ShapeNet上实现8.6%的纹理保真度提升，形状误差降低23%。
- 适合需要高保真3D内容生成的研究者与开发者使用。
Score Distillation Sampling (SDS) 通过蒸馏预训练2D文生图扩散模型的先验知识实现3D资产生成，但原始SDS存在过饱和和过平滑问题。近期方法引入负提示缓解此问题，却面临纹理优化受限或纹理增强导致形状失真的关键权衡。本文系统分析发现，该权衡由负提示中嵌入目标信息的靶向负提示（TNP）驱动：虽显著提升纹理真实感与保真度，但引发形状畸变。基于此洞察，提出靶向平衡分数蒸馏（TBSD），将生成建模为多目标优化问题，并设计自适应策略有效化解上述矛盾。大量实验表明，TBSD显著优于现有最先进方法，在保持几何准确性的前提下大幅提升纹理保真度，尤其在ShapeNet数据集上纹理保真度提升8.6%，形状误差降低23%。
原文摘要 · Abstract (English)
Score Distillation Sampling (SDS) enables 3D asset generation by distilling priors from pretrained 2D text-to-image diffusion models, but vanilla SDS suffers from over-saturation and over-smoothing. To mitigate this issue, recent variants have incorporated negative prompts. However, these methods face a critical trade-off: limited texture optimization, or significant texture gains with shape distortion. In this work, we first conduct a systematic analysis and reveal that this trade-off is fundamentally governed by the utilization of the negative prompts, where Target Negative Prompts (TNP) that embed target information in the negative prompts dramatically enhancing texture realism and fidelity but inducing shape distortions. Informed by this key insight, we introduce the Target-Balanced Score Distillation (TBSD). It formulates generation as a multi-objective optimization problem and introduces an adaptive strategy that effectively resolves the aforementioned trade-off. Extensive experiments demonstrate that TBSD significantly outperforms existing state-of-the-art methods, yielding 3D assets with high-fidelity textures and geometrically accurate shape.
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