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Abstract: Explicit 3D shape abstraction from a single 2D image is a long-standing problem in computer vision and graphics. By leveraging a set of primitives to represent the target shape, recent methods have achieved promising results. However, these methods either use a relatively larger number of primitives or lack geometric flexibility due to the low-dimensional expressibility of the primitives. In this study, we propose a novel bi-channel Transformer architecture, integrated with parameterized deformable models, termed DeFormer, to simultaneously estimate global and local deformations. In this way, DeFormer can abstract complex object shapes while using a small number of primitives which offer a broader geometry coverage and finer details. Then, we introduce a force-driven dynamic fitting and a cycle-consistent re-projection loss to optimize the primitive parameters. Extensive experiments on ShapeNet across various settings show that DeFormer achieves on average 3.8$%$ better reconstruction accuracy over the state-of-the-art, and visualize with consistent semantic correspondences for improved interpretability. Experiments on medical datasets including ACDC, M&Ms, and M&Ms-2 further show the generalization ability of DeFormer for object segmentation across different domains.