no code implementations • 24 Feb 2023 • Cusuh Ham, James Hays, Jingwan Lu, Krishna Kumar Singh, Zhifei Zhang, Tobias Hinz
We show that MCM enables user control over the spatial layout of the image and leads to increased control over the image generation process.
no code implementations • CVPR 2023 • Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, Kun Zhang
By contrast, multi-modal inpainting provides more flexible and useful controls on the inpainted content, \eg, a text prompt can be used to describe an object with richer attributes, and a mask can be used to constrain the shape of the inpainted object rather than being only considered as a missing area.
1 code implementation • 24 May 2022 • Difan Liu, Sandesh Shetty, Tobias Hinz, Matthew Fisher, Richard Zhang, Taesung Park, Evangelos Kalogerakis
We present ASSET, a neural architecture for automatically modifying an input high-resolution image according to a user's edits on its semantic segmentation map.
1 code implementation • 5 Feb 2021 • Tobias Hinz, Matthew Fisher, Oliver Wang, Eli Shechtman, Stefan Wermter
Our model generates novel poses based on keypoint locations, which can be modified in real time while providing interactive feedback, allowing for intuitive reposing and animation.
no code implementations • 25 Jan 2021 • Stanislav Frolov, Tobias Hinz, Federico Raue, Jörn Hees, Andreas Dengel
With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area.
1 code implementation • 24 Jun 2020 • Stefan Heinrich, Yuan YAO, Tobias Hinz, Zhiyuan Liu, Thomas Hummel, Matthias Kerzel, Cornelius Weber, Stefan Wermter
From a neuroscientific perspective, natural language is embodied, grounded in most, if not all, sensory and sensorimotor modalities, and acquired by means of crossmodal integration.
3 code implementations • 25 Mar 2020 • Tobias Hinz, Matthew Fisher, Oliver Wang, Stefan Wermter
Recently there has been an interest in the potential of learning generative models from a single image, as opposed to from a large dataset.
2 code implementations • 29 Oct 2019 • Tobias Hinz, Stefan Heinrich, Stefan Wermter
To address these challenges we introduce a new model that explicitly models individual objects within an image and a new evaluation metric called Semantic Object Accuracy (SOA) that specifically evaluates images given an image caption.
Ranked #53 on Text-to-Image Generation on MS COCO
1 code implementation • 21 Aug 2019 • Marcus Soll, Tobias Hinz, Sven Magg, Stefan Wermter
Adversarial examples are artificially modified input samples which lead to misclassifications, while not being detectable by humans.
1 code implementation • ICLR 2019 • Tobias Hinz, Stefan Heinrich, Stefan Wermter
Our experiments show that through the use of the object pathway we can control object locations within images and can model complex scenes with multiple objects at various locations.
Ranked #64 on Text-to-Image Generation on MS COCO
no code implementations • 19 Jul 2018 • Tobias Hinz, Nicolás Navarro-Guerrero, Sven Magg, Stefan Wermter
This is independent of the underlying optimization procedure, making the approach promising for many existing hyperparameter optimization algorithms.
no code implementations • 28 Mar 2018 • Tobias Hinz, Stefan Wermter
We train an encoder to encode images into these representations and use a small amount of labeled data to specify what kind of information should be encoded in the disentangled part.
2 code implementations • 7 Mar 2018 • 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.
Ranked #5 on Unsupervised Image Classification on MNIST