3D-aware scene manipulation via inverse graphics
Author(s)Yao, Shunyu; Hsu, Tzu Ming; Zhu, Jun-Yan; Wu, Jiajun; Torralba, Antonio; Freeman, William T.; Tenenbaum, Joshua B.; ... Show more Show less
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We aim to obtain an interpretable, expressive, and disentangled scene representation that contains comprehensive structural and textural information for each object. Previous scene representations learned by neural networks are often uninterpretable, limited to a single object, or lacking 3D knowledge. In this work, we propose 3D scene de-rendering networks (3D-SDN) to address the above issues by integrating disentangled representations for semantics, geometry, and appearance into a deep generative model. Our scene encoder performs inverse graphics, translating a scene into a structured object-wise representation. Our decoder has two components: a differentiable shape renderer and a neural texture generator. The disentanglement of semantics, geometry, and appearance supports 3D-aware scene manipulation, e.g., rotating and moving objects freely while keeping the consistent shape and texture, and changing the object appearance without affecting its shape. Experiments demonstrate that our editing scheme based on 3D-SDN is superior to its 2D counterpart. ©2018 Poster presentation at the 32nd annual Conference on Neural Information Processing Systems (NIPS 2018), December 3-5, 2018, Montréal, Québec.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Advances in Neural Information Processing Systems
Neural Information Processing Systems Foundation, Inc.
Yao, Shunyu, et al., "3D-aware scene manipulation via inverse graphics." Advances in Neural Information Processing Systems 31 (2018) url https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018
Final published version