While generative AI has unlocked new opportunities for 3D content creation, current workflows often rely on multiple regenerations, which provides limited control and unpredictable outcomes. We present Compos3D, a system that introduces a compositional workflow for generative 3D modeling through remixing. Instead of repeatedly regenerating models, users generate multiple candidates from text or image prompts, select parts of interest via 2D image regions or 3D mesh segments, and assemble them into a coherent design. The system synthesizes these compositions into a refined 3D model, preserving high-level intent while resolving low-level geometry. To evaluate this approach, we conducted a controlled user study comparing remixing and regeneration workflows across both 2D and 3D modalities. Results show that the remixing workflow provides participants with greater creative control, stronger alignment with their intent, and higher satisfaction. We conclude with design recommendations for future AI-assisted 3D modeling workflows.
Recent advances in generative AI allow users to create 3D models from text or images. However, these models prioritize visual plausibility over geometric accuracy, often generating results with flaws that compromise their intended use post-fabrication. We present InstructMesh, an interactive post-generation refinement...
Faraz Faruqi, Ahmed Katary, Demircan Tas et al.· 0 citations
Generative AI lets anyone create rich visual content in seconds, yet translating that content into a physically fabricable artifact still demands manual decomposition, occlusion repair, and structural verification that most tools leave entirely to the user. We present FabDreamer, an image-to-physical system that carrie...
Chenfeng Gao, Zeya Chen, An Yang et al.· 0 citations
Experiments demonstrate that the MultiCube method can generate high-quality compositional 3D objects with precise part-level control, including those with unique layouts difficult to achieve with text or image prompting alone.
Ava Pun, Kang-Le Deng, Yi-Heng Zhu et al.· 0 citations
3D scene generation has rapidly evolved, significantly promoting the innovation of content creation. In this context, interaction techniques serve as a pivotal bridge connecting user intent with the generative models, thereby enabling precise control, real-time feedback and personalized customization of complex 3D scen...
Yuqi Li, Si-Wei Meng, Chuan-Guang Yang et al.· Proceedings of the Thirty-Fi...· 33 citations· ⚡1
3D content generation technology has significantly advanced the work of designers, as well as the 3D printing and gaming industries. However, it remains difficult to produce lightweight, editable, and topologically clean artistic content that is directly production-ready. To achieve this, we present TaoFlowForge, an ar...
Xian-Ze Fang, Qi-Yuan Feng, Dongfang Sun et al.· 0 citations
The convergence of diffusion models and 3D Gaussian Splatting (3D -GS) has catalyzed a paradigm shift in AI-driven 3D/4D content creation, enabling high-quality generation, editing, and animation from natural language instructions. This narrative review syn thesizes representative works published between 2023 and 2025...
