The collected images cover a wider variety of human activities than previous datasets including various recreational, occupational and householding activ-ities, and capture people from a wider range of viewpoints. This comprehensive dataset was collected using an established taxonomy of over 800 human activities. In this paper we intro-duce a novel benchmark "MPII Human Pose" 1 that makes a significant advance in terms of diversity and difficulty, a contribution that we feel is required for future develop-ments in human body models. Still these serve as the common sources to evaluate, train and compare different models on. However current datasets are limited in their coverage of the overall pose estimation challenges. Human pose estimation has made significant progress during the last years. Experiments on two benchmark datasets show that our method is capable of generating visually appealing and realistic-looking results using arbitrary image and pose inputs. In addition, we present a new normalization method named adaptive patch normalization, which enables region-specific normalization and shows a good performance when adopted in person image generation model. The framework is highly flexible and controllable by effectively decoupling various complex person image factors in the encoding phase, followed by re-coupling them in the decoding phase.
Stylizer 6 serial number generator#
The core of our framework is a novel generator called Appearance-aware Pose Stylizer (APS) which generates human images by coupling the target pose with the conditioned person appearance progressively.
In this paper, we present a novel end-to-end framework to generate realistic person images based on given person poses and appearances. Generation of high-quality person images is challenging, due to the sophisticated entanglements among image factors, e.g., appearance, pose, foreground, background, local details, global structures, etc.
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