Janne Hellsten
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View article: Analyzing and Improving the Training Dynamics of Diffusion Models
Analyzing and Improving the Training Dynamics of Diffusion Models Open
Diffusion models currently dominate the field of data-driven image synthesis with their unparalleled scaling to large datasets. In this paper, we identify and rectify several causes for uneven and ineffective training in the popular ADM di…
View article: Generating Long Videos of Dynamic Scenes
Generating Long Videos of Dynamic Scenes Open
We present a video generation model that accurately reproduces object motion, changes in camera viewpoint, and new content that arises over time. Existing video generation methods often fail to produce new content as a function of time whi…
View article: Alias-Free Generative Adversarial Networks
Alias-Free Generative Adversarial Networks Open
We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner. This manifests itself as, e.g., detail appearin…
View article: Alias-Free Generative Adversarial Networks
Alias-Free Generative Adversarial Networks Open
We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner. This manifests itself as, e.g., detail appearin…
View article: Modular primitives for high-performance differentiable rendering
Modular primitives for high-performance differentiable rendering Open
We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics …
View article: Training Generative Adversarial Networks with Limited Data
Training Generative Adversarial Networks with Limited Data Open
Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes train…
View article: Analyzing and Improving the Image Quality of StyleGAN
Analyzing and Improving the Image Quality of StyleGAN Open
The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architectu…