PartConverter: A Part‐Oriented Transformation Framework for Point Clouds

ABSTRACT With generative AI technologies advancing rapidly, the capabilities for 3D model generation and transformation are expanding across industries like manufacturing, healthcare, and virtual reality. However, existing methods based on generative adversarial networks (GANs), autoencoders, or transformers still have notable limitations. They primarily generate entire objects without providing flexibility for independent part transformation or precise control over model components. These constraints pose challenges for applications requiring complex object manipulation and fine‐grained adjustments. To overcome these limitations, we propose PartConverter, a novel part‐oriented point cloud transformation framework emphasizing flexibility and precision in 3D model transformations. PartConverter leverages attention mechanisms and autoencoders to capture crucial details within each part while modeling the relationships between components, thereby enabling highly customizable, part‐wise transformations that maintain overall consistency. Additionally, our part assembler ensures that transformed parts align coherently, resulting in a consistent and realistic final 3D shape. This framework significantly enhances control over detailed part modeling, increasing the flexibility and efficiency of 3D model transformation workflows.