PartCrafter is a new AI-based tool that can create several geometrically and semantically distinguishable 3D objects from a single image. The special feature: The generated models do not consist of a single coherent mesh, but of clearly delineated, structured individual parts. This makes the process particularly suitable for applications where modular or interactive 3D models are required – for example in game development, robotics or CAD-supported design.
In contrast to conventional approaches, which usually generate an entire object or rely on a pre-segmented input, PartCrafter processes unprocessed images directly and breaks them down internally into individual components. This is made possible by a specially developed two-stage structure. First, the model creates a latent representation with separable tokens for each object component. These tokens are supplemented by so-called identity embeddings, which assign semantic labels to the parts. A hierarchical attention structure then ensures coordination within and between the individual parts.
The technical basis is a pre-trained diffusion transformer model that was originally designed for complete meshes. Building on this, PartCrafter extends the architecture with functions for part recognition and generation. A specially curated data set with around 50,000 objects and 300,000 individual parts was used for training. Attention was paid to good texture quality and precise part labeling.
In tests, PartCrafter was not only able to generate clean and structurally coherent models, but was also able to plausibly reconstruct missing or hidden parts. The publication of the code and the training data is intended to support future developments in the field of structured 3D generation and provide researchers with a solid starting point.
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