Search Results for author: Qiyang Hu

Found 7 papers, 2 papers with code

Smart, Deep Copy-Paste

no code implementations15 Mar 2019 Tiziano Portenier, Qiyang Hu, Paolo Favaro, Matthias Zwicker

In this work, we propose a novel system for smart copy-paste, enabling the synthesis of high-quality results given a masked source image content and a target image context as input.

Learning to Take Directions One Step at a Time

1 code implementation5 Dec 2018 Qiyang Hu, Adrian Wälchli, Tiziano Portenier, Matthias Zwicker, Paolo Favaro

Because items in an image can be animated in arbitrarily many different ways, we introduce as control signal a sequence of motion strokes.

Video Prediction

Understanding Degeneracies and Ambiguities in Attribute Transfer

no code implementations ECCV 2018 Attila Szabo, Qiyang Hu, Tiziano Portenier, Matthias Zwicker, Paolo Favaro

We study the problem of building models that can transfer selected attributes from one image to another without affecting the other attributes.

FaceShop: Deep Sketch-based Face Image Editing

no code implementations24 Apr 2018 Tiziano Portenier, Qiyang Hu, Attila Szabó, Siavash Arjomand Bigdeli, Paolo Favaro, Matthias Zwicker

We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest.

Image Manipulation

Disentangling Factors of Variation by Mixing Them

no code implementations CVPR 2018 Qiyang Hu, Attila Szabó, Tiziano Portenier, Matthias Zwicker, Paolo Favaro

We learn our representation without any labeling or knowledge of the data domain, using an autoencoder architecture with two novel training objectives: first, we propose an invariance objective to encourage that encoding of each attribute, and decoding of each chunk, are invariant to changes in other attributes and chunks, respectively; second, we include a classification objective, which ensures that each chunk corresponds to a consistently discernible attribute in the represented image, hence avoiding degenerate feature mappings where some chunks are completely ignored.

General Classification

Challenges in Disentangling Independent Factors of Variation

2 code implementations ICLR 2018 Attila Szabó, Qiyang Hu, Tiziano Portenier, Matthias Zwicker, Paolo Favaro

Such models could be used to encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis.

Image Generation

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