Look, Evolve and Mold: Learning 3D Shape Manifold via Single-view Synthetic Data
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With daily observation and prior knowledge, it is easy for us human to infer the stereo structure via a single view. However, to equip the deep models with such ability usually requires abundant supervision. It is promising that without the elaborated 3D annotation, we can simply profit from the synthetic data, where pairwise ground-truth is easy to access. Nevertheless, the domain gap is not neglectable considering the variant texture, shape and context. To overcome these difficulties, we propose a domain-adaptive network for single-view 3D reconstruction, dubbed LEM, to generalize towards the natural scenario by fulfilling several aspects: (1) Look: incorporating spatial structure from the single view to enhance the representation; (2) Evolve: leveraging the semantic information with unsupervised contrastive mapping recurring to the shape priors; (3) Mold: transforming into the desired stereo manifold with discernment and semantic knowledge. Extensive experiments on several benchmarks demonstrate the effectiveness and robustness of the proposed method, LEM, in learning the 3D shape manifold from the synthetic data via a single-view.
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