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This paper introduces a deformation learning framework for monocular 3D shape recovery that explicitly deforms a category-level shape template to match a target observation. The core innovation is a geometry-guided feature modeling mechanism that enriches foundation features with template topology and correlates them with the target observation for precise deformation. A view-adaptive feature aggregation module further improves generalization by leveraging multi-view template features and camera poses to handle varying viewpoints.
Achieve robust 3D shape recovery across viewpoints and unseen object categories by explicitly modeling and correlating template geometry with target observations.
Monocular 3D shape recovery is fundamental to geometric understanding, yet achieving robust generalization across arbitrary viewpoints and unseen object categories remains a significant challenge. In this paper, we present a generalizable deformation learning framework that reconstructs 3D objects by explicitly deforming a category-level shape template to match the target observation. To address complex shape variations between the template and the target, we introduce a geometry-guided feature modeling mechanism. This process first enriches foundation features with template topology to yield a geometry-aware representation, which is then explicitly correlated with the target observation to guide precise deformation. Furthermore, to bridge the disparity between the fixed template and arbitrary target views, we propose a view-adaptive feature aggregation module. This module leverages multi-view template features and their corresponding camera poses to enrich the canonical template representation, ensuring robust feature alignment regardless of the target's perspective. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods in handling large shape variations and diverse viewpoints, exhibiting strong generalization to novel categories and effectively supporting downstream real-world dexterous robotic manipulation tasks. Project homepage: https://GODeform.github.io/
