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Found 8 papers, 7 papers with code
Persistent Nature: A Generative Model of Unbounded 3D Worlds
Despite increasingly realistic image quality, recent 3D image generative models often operate on 3D volumes of fixed extent with limited camera motions.
Ranked #2 on
Scene Generation
on GoogleEarth
DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data
Furthermore, our metric outperforms both prior learned metrics and recent large vision models on these tasks.
On the "steerability" of generative adversarial networks
We hypothesize that the degree of distributional shift is related to the breadth of the training data distribution.
Any-resolution Training for High-resolution Image Synthesis
To take advantage of varied-size data, we introduce continuous-scale training, a process that samples patches at random scales to train a new generator with variable output resolutions.
Using latent space regression to analyze and leverage compositionality in GANs
In this work, we investigate regression into the latent space as a probe to understand the compositional properties of GANs.
What makes fake images detectable? Understanding properties that generalize
The quality of image generation and manipulation is reaching impressive levels, making it increasingly difficult for a human to distinguish between what is real and what is fake.
Ensembling with Deep Generative Views
Here, we investigate whether such views can be applied to real images to benefit downstream analysis tasks such as image classification.
Totems: Physical Objects for Verifying Visual Integrity
We introduce a new approach to image forensics: placing physical refractive objects, which we call totems, into a scene so as to protect any photograph taken of that scene.
