  Accurate 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 large number of primitives or
lack geometric flexibility due to the limited expressibility of the primitives.
In this paper, we propose a novel bi-channel Transformer architecture,
integrated with parameterized deformable models, termed DeFormer, to
simultaneously estimate the global and local deformations of primitives. 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 better
reconstruction accuracy over the state-of-the-art, and visualizes with
consistent semantic correspondences for improved interpretability.
