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• W4365420462 abstract "Neural Radiance Fields (NeRF) learn a model for the high-quality 3D-view reconstruction of a single object. Category-specific representation makes it possible to generalize to the reconstruction and even generation of multiple objects. Existing efforts mainly focus on the reconstruction performance including speed and quality. The steerability of generation processes has not been well studied while semantic attributes still exist in 3D neural representations. Inspired by interpreting underlying factors of GANs, this paper proposes a novel method named EigenGRF to disentangle the latent semantic subspace in an unsupervised manner. By learning a set of eigenbasis, we can readily control the process and the result of object synthesis accordingly. Concretely, our method brings a mapping network to NeRF by conditioning on a FiLM-SIREN layer. Then we use a component analysis method for discovering steerable latent subspaces. Our experiments reveal that the proposed method is powerful for the 3D-aware generation with steerability by both synthetic and real-world datasets." @default.
• W4365420462 created "2023-04-14" @default.
• W4365420462 creator A5000546219 @default.
• W4365420462 creator A5018988672 @default.
• W4365420462 date "2023-01-01" @default.
• W4365420462 modified "2023-09-28" @default.
• W4365420462 title "EigenGRF: Layer-Wise Eigen-Learning for Controllable Generative Radiance Fields" @default.
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• W4365420462 doi "https://doi.org/10.1007/978-3-031-30105-6_16" @default.
• W4365420462 hasPublicationYear "2023" @default.
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