Bioinspired123D: Generative 3D Modeling System for Bioinspired Structures

• URL: http://arxiv.org/abs/2603.29592v1
• Date: Wed, 11 Feb 2026 15:49:40 GMT
• Title: Bioinspired123D: Generative 3D Modeling System for Bioinspired Structures
• Abstract summary: We introduce Bioinspired123D, a lightweight and modular code-as-geometry pipeline that generates fabricable 3D structures directly through parametric programs.<n>At the core of Bioinspired123D is Bioinspired3D, a compact language model finetuned to translate natural language design cues into Blender Python scripts.<n>We evaluate performance on a new benchmark for 3D geometry script generation and show that Bioinspired123D demonstrates a near fourfold improvement over its non-finetuned base model.
• Score: 1.1458853556386797
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Generative AI has made rapid progress in text, image, and video synthesis, yet text-to-3D modeling for scientific design remains particularly challenging due to limited controllability and high computational cost. Most existing 3D generative methods rely on meshes, voxels, or point clouds which can be costly to train and difficult to control. We introduce Bioinspired123D, a lightweight and modular code-as-geometry pipeline that generates fabricable 3D structures directly through parametric programs rather than dense visual representations. At the core of Bioinspired123D is Bioinspired3D, a compact language model finetuned to translate natural language design cues into Blender Python scripts encoding smooth, biologically inspired geometries. We curate a domain-specific dataset of over 4,000 bioinspired and geometric design scripts spanning helical, cellular, and tubular motifs with parametric variability. The dataset is expanded and validated through an automated LLM-driven, Blender-based quality control pipeline. Bioinspired3D is then embedded in a graph-based agentic framework that integrates multimodal retrieval-augmented generation and a vision-language model critic to iteratively evaluate, critique, and repair generated scripts. We evaluate performance on a new benchmark for 3D geometry script generation and show that Bioinspired123D demonstrates a near fourfold improvement over its non-finetuned base model, while also outperforming substantially larger state-of-the-art language models despite using far fewer parameters and compute. By prioritizing code-as-geometry representations, Bioinspired123D enables compute-efficient, controllable, and interpretable text-to-3D generation, lowering barriers to AI driven scientific discovery in materials and structural design.

Related papers

• 3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code [25.904997126319547]
  3DCodeBench is a systematic benchmark for evaluating vision-language model (VLM) agents for procedural 3D generation in 3D modeling software.<n>We build 3DCodeArena, a ranking platform based on pairwise human preferences over generated 3D outputs.
  arXiv  Detail & Related papers  (2026-05-31T06:59:49Z)
• Know3D: Prompting 3D Generation with Knowledge from Vision-Language Models [39.48274812723658]
  Know3D is a novel framework that incorporates rich knowledge from large language models into 3D generative processes.<n>We successfully bridge the gap between abstract instructions and the geometric reconstruction of unobserved regions.
  arXiv  Detail & Related papers  (2026-03-24T04:18:29Z)
• VIST3A: Text-to-3D by Stitching a Multi-view Reconstruction Network to a Video Generator [69.72818094722186]
  A text-to-video generator can be combined with a 3D reconstruction system as a "decoder"<n>We introduce VIST3A, a general framework that does just that.<n>We evaluate the proposed VIST3A approach with different video generators and 3D reconstruction models.
  arXiv  Detail & Related papers  (2025-10-15T11:55:08Z)
• End-to-End Fine-Tuning of 3D Texture Generation using Differentiable Rewards [8.953379216683732]
  We propose an end-to-end differentiable, reinforcement-learning-free framework that embeds human feedback, expressed as differentiable reward functions, directly into the 3D texture pipeline.<n>By back-propagating preference signals through both geometric and appearance modules, our method generates textures that respect the 3D geometry structure and align with desired criteria.
  arXiv  Detail & Related papers  (2025-06-23T06:24:12Z)
• Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets [90.99212668875971]
  Step1X-3D is an open framework addressing challenges such as data scarcity, algorithmic limitations, and ecosystem fragmentation.<n>We present a two-stage 3D-native architecture combining a hybrid VAE-DiT geometry generator with a diffusion-based texture synthesis module.<n> Benchmark results demonstrate state-of-the-art performance that exceeds existing open-source methods.
  arXiv  Detail & Related papers  (2025-05-12T16:56:30Z)
• Eval3D: Interpretable and Fine-grained Evaluation for 3D Generation [134.53804996949287]
  We introduce Eval3D, a fine-grained, interpretable evaluation tool that can faithfully evaluate the quality of generated 3D assets.<n>Our key observation is that many desired properties of 3D generation, such as semantic and geometric consistency, can be effectively captured.<n>Compared to prior work, Eval3D provides pixel-wise measurement, enables accurate 3D spatial feedback, and aligns more closely with human judgments.
  arXiv  Detail & Related papers  (2025-04-25T17:22:05Z)
• Structured 3D Latents for Scalable and Versatile 3D Generation [28.672494137267837]
  We introduce a novel 3D generation method for versatile and high-quality 3D asset creation.<n>The cornerstone is a unified Structured LATent representation which allows decoding to different output formats.<n>This is achieved by integrating a sparsely-populated 3D grid with dense multiview visual features extracted from a powerful vision foundation model.
  arXiv  Detail & Related papers  (2024-12-02T13:58:38Z)
• GaussianAnything: Interactive Point Cloud Flow Matching For 3D Object Generation [75.39457097832113]
  This paper introduces a novel 3D generation framework, offering scalable, high-quality 3D generation with an interactive Point Cloud-structured Latent space.<n>Our framework employs a Variational Autoencoder with multi-view posed RGB-D(epth)-N(ormal) renderings as input, using a unique latent space design that preserves 3D shape information.<n>The proposed method, GaussianAnything, supports multi-modal conditional 3D generation, allowing for point cloud, caption, and single image inputs.
  arXiv  Detail & Related papers  (2024-11-12T18:59:32Z)
• L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects [53.4874127399702]
  We propose a language agent with chain-of-3D-thoughts (L3GO), an inference-time approach that can reason about part-based 3D mesh generation. We develop a new benchmark, Unconventionally Feasible Objects (UFO), as well as SimpleBlenv, a wrapper environment built on top of Blender. Our approach surpasses the standard GPT-4 and other language agents for 3D mesh generation on ShapeNet.
  arXiv  Detail & Related papers  (2024-02-14T09:51:05Z)
• IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation [96.32684334038278]
  In this paper, we explore the design space of text-to-3D models. We significantly improve multi-view generation by considering video instead of image generators. Our new method, IM-3D, reduces the number of evaluations of the 2D generator network 10-100x.
  arXiv  Detail & Related papers  (2024-02-13T18:59:51Z)
• ControlDreamer: Blending Geometry and Style in Text-to-3D [34.92628800597151]
  We introduce multi-view ControlNet, a novel depth-aware multi-view diffusion model trained on datasets from a carefully curated text corpus. Our multi-view ControlNet is then integrated into our two-stage pipeline, ControlDreamer, enabling text-guided generation of stylized 3D models.
  arXiv  Detail & Related papers  (2023-12-02T13:04:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.