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While recent NeRF-based generative models achieve the generation of diverse 3D-aware images, these approaches have limitations when generating images that contain user-specified characteristics.
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Improved precision and recall metric for assessing generative models
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GRAF: Generative radiance fields for 3d-aware image synthesis
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Df-gan: Deep fusion generative adversarial networks for text-to-image synthesis
Tao, M.; Tang, H.; Wu, S.; Sebe, N.; Jing, X.-Y.; Wu, F.; and Bao, B. 2020 · 2008
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The caltech-ucsd birds-200-2011 dataset
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Image-to-image translation with conditional adversarial networks
Isola, P.; Zhu, J.-Y.; Zhou, T.; and Efros, A. A. 2017 · 2017
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Karras, T.; Aila, T.; Laine, S.; and Lehtinen, J. 2017 · 2017
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Which training methods for GANs do actually converge?
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Nerf: Representing scenes as neural radiance fields for view synthesis
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Neural scene flow fields for space-time view synthesis of dynamic scenes
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Reliable Fidelity and Diversity Metrics for Generative Models
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High-fidelity performance metrics for generative models in PyTorch
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Implicit neural representations with periodic activation functions
Sitzmann, V.; Martel, J.; Bergman, A.; Lindell, D.; and Wetzstein, G. 2020 · 2020
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pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis
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Baking Neural Radiance Fields for Real-Time View Synthesis
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CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields
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