Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D Generation
Nan Xiang, Tianyi Liang, Haiwen Huang, Shiqi Jiang, Hao Huang, Yifei Huang, Liangyu Chen, Changbo Wang, Chenhui Li
摘要
Text-to-3D (T23D) generation has transformed digital content creation, yet remains bottlenecked by blind trial-and-error prompting processes that yield unpredictable results. While visual prompt engineering has advanced in text-to-image domains, its application to 3D generation presents unique challenges requiring multi-view consistency evaluation and spatial understanding. We present Sel3DCraft, a visual prompt engineering system for T23D that transforms unstructured exploration into a guided visual process. Our approach introduces three key innovations: a dual-branch structure combining retrieval and generation for diverse candidate exploration; a multi-view hybrid scoring approach that leverages MLLMs with innovative high-level metrics to assess 3D models with human-expert consistency; and a prompt-driven visual analytics suite that enables intuitive defect identification and refinement. Extensive testing and a user study demonstrate that Sel3DCraft surpasses other T23D systems in supporting creativity for designers.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper2
- Consensus Entropy: Harnessing Multi-VLM Agreement for Self-Verifying and Self-Improving OCRYulong Zhang, Tianyi Liang, Erfei Cui, Guoqing Wang 等CVPR 2026 · 被引用 14 次
- Preference-Guided Prompt Optimization for Text-to-Image GenerationZhipeng Li, Yi-Chi Liao, Christian HolzCHI 2026 · 被引用 1 次
它引用的顶会 Paper41
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 被引用 64,255 次
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida 等NeurIPS 2022 · 被引用 24,707 次
- Segment AnythingAlexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao 等ICCV 2023 · 被引用 13,211 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
相关 Paper
- Multimodal Semantic Bias Mitigation for Diverse Text-To-3D GenerationYukuan Min, Muli Yang, Jinhao Zhang, Yuxuan Wang 等CVPR 2026
- VP3D: Unleashing 2D Visual Prompt for Text-to-3D GenerationYang Chen, Yingwei Pan, Haibo Yang, Ting Yao 等CVPR 2024
- PromptMagician: Interactive Prompt Engineering for Text-to-Image CreationYingchaojie Feng, Xingbo Wang, Kamkwai Wong, Sijia Wang 等IEEE VIS 2023 · 被引用 127 次
- Vinedresser3D: Towards Agentic Text-guided 3D EditingYankuan Chi, Xiang Li, Zixuan Huang, James M.CVPR 2026
- Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMsYumin Choi, Dongki Kim, Jinheon Baek, Sung Ju HwangICLR 2026 · 被引用 4 次
