Understanding and generating the fine-grained structure of objects -- such as birds with species-specific beaks, wings, and tails -- is a long-standing challenge in computer vision. We propose Chirpy3D, a part-aware multi-view diffusion framework that learns a hierarchical part latent space from unposed 2D images, using only off-the-shelf 2D part segmentation masks as spatial guidance -- without requiring any 3D data, camera poses, or manual part annotations. This latent space enables intuitive part-level swapping, interpolation, and zero-shot composition. A self-supervised feature consistency loss further encourages structural alignment across views, allowing coherent generation even with hybrid or unseen part combinations. Our core contribution is the controllable part-aware latent space and multi-view diffusion model. Downstream 3D generation is supported via any differentiable renderer such as NeRF but is orthogonal to the main framework, making Chirpy3D a flexible foundation for creative object generation in the absence of structured 3D data. Code is released at https://github.com/kamwoh/chirpy3d.

翻译：理解和生成物体的细粒度结构——例如具有物种特异性喙、翅膀和尾巴的鸟类——是计算机视觉中一项长期存在的挑战。我们提出Chirpy3D，一种部件感知的多视角扩散框架，该框架仅利用现成的二维部件分割掩膜作为空间引导，从无姿态的二维图像中学习层次化的部件隐空间——无需任何三维数据、相机姿态或人工部件标注。该隐空间能够实现直观的部件级交换、插值以及零样本组合。一种自监督的特征一致性损失进一步促进了跨视角的结构对齐，即使面对混合或未见过的部件组合也能生成连贯结果。我们的核心贡献在于可控的部件感知隐空间和多视角扩散模型。下游的三维生成通过任意可微分渲染器（如NeRF）得以支持，但这与主体框架正交，这使得Chirpy3D成为在缺乏结构化三维数据情况下进行创意物体生成的灵活基础。代码已开源在https://github.com/kamwoh/chirpy3d。