标题： Chirpy3D：通过部件采样进行创意细粒度3D物体制造 显示英文标题

标题： Chirpy3D: Creative Fine-grained 3D Object Fabrication via Part Sampling

摘要： 我们提出Chirpy3D，一种用于细粒度3D物体生成的新方法，解决在零样本设置下合成创造性的3D物体的挑战性任务，仅能访问已见类别的无姿势2D图像。 在没有结构化监督的情况下——如相机姿态、3D部件注释或特定物体标签——模型必须推断出合理的3D结构，捕捉细粒度细节，并仅使用已见类别的类别级标签来推广到新物体。 为了解决这个问题，Chirpy3D引入了一个多视图扩散模型，以无监督的方式将训练物体分解为锚定部件，将已见和未见部件的潜在空间表示为连续分布。 这使得部件可以平滑插值和灵活重组，生成具有物种特异性细节的全新物体。 一种自监督特征一致性损失进一步确保了结构和语义的一致性。 结果是第一个能够通过灵活的部件采样和组合生成完全新颖的3D物体并具有物种特异性细粒度细节的系统。 我们的实验表明，Chirpy3D在生成高质量且具有细粒度细节的创造性3D物体方面优于现有方法。 代码将在https://github.com/kamwoh/chirpy3d发布。 显示英文摘要

摘要： We present Chirpy3D, a novel approach for fine-grained 3D object generation, tackling the challenging task of synthesizing creative 3D objects in a zero-shot setting, with access only to unposed 2D images of seen categories. Without structured supervision -- such as camera poses, 3D part annotations, or object-specific labels -- the model must infer plausible 3D structures, capture fine-grained details, and generalize to novel objects using only category-level labels from seen categories. To address this, Chirpy3D introduces a multi-view diffusion model that decomposes training objects into anchor parts in an unsupervised manner, representing the latent space of both seen and unseen parts as continuous distributions. This allows smooth interpolation and flexible recombination of parts to generate entirely new objects with species-specific details. A self-supervised feature consistency loss further ensures structural and semantic coherence. The result is the first system capable of generating entirely novel 3D objects with species-specific fine-grained details through flexible part sampling and composition. Our experiments demonstrate that Chirpy3D surpasses existing methods in generating creative 3D objects with higher quality and fine-grained details. Code will be released at https://github.com/kamwoh/chirpy3d.