207 citations · 529 across the 10 of their papers we have counts for
9 papers · 1 filter
Modulating Pretrained Diffusion Models for Multimodal Image Synthesis
Cusuh Ham, James Hays, Jingwan Lu +3
We present multimodal conditioning modules (MCM) for enabling conditional image synthesis using pretrained diffusion models. Previous multimodal synthesis works rely on training ne…
SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model
Shaoan Xie, Zhifei Zhang, Zhe Lin +2
Generic image inpainting aims to complete a corrupted image by borrowing surrounding information, which barely generates novel content. By contrast, multi-modal inpainting provides…
CharacterGAN: Few-Shot Keypoint Character Animation and Reposing
Tobias Hinz, Matthew Fisher, Oliver Wang +2
We introduce CharacterGAN, a generative model that can be trained on only a few samples (8 - 15) of a given character. Our model generates novel poses based on keypoint locations,…
Adversarial Text-to-Image Synthesis: A Review
Stanislav Frolov, Tobias Hinz, Federico Raue +2
With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area. It is a flexible and intuitive way fo…
Improved Techniques for Training Single-Image GANs
Tobias Hinz, Matthew Fisher, Oliver Wang +1
Recently there has been an interest in the potential of learning generative models from a single image, as opposed to from a large dataset. This task is of practical significance,…
Semantic Object Accuracy for Generative Text-to-Image Synthesis
Tobias Hinz, Stefan Heinrich, Stefan Wermter
Generative adversarial networks conditioned on textual image descriptions are capable of generating realistic-looking images. However, current methods still struggle to generate im…
Generating Multiple Objects at Spatially Distinct Locations
Tobias Hinz, Stefan Heinrich, Stefan Wermter
Recent improvements to Generative Adversarial Networks (GANs) have made it possible to generate realistic images in high resolution based on natural language descriptions such as i…
Image Generation and Translation with Disentangled Representations
Tobias Hinz, Stefan Wermter
Generative models have made significant progress in the tasks of modeling complex data distributions such as natural images. The introduction of Generative Adversarial Networks (GA…
Inferencing Based on Unsupervised Learning of Disentangled Representations
Tobias Hinz, Stefan Wermter
Combining Generative Adversarial Networks (GANs) with encoders that learn to encode data points has shown promising results in learning data representations in an unsupervised way.…