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Xinggan for Person Image Generation
XingGAN for Person Image Generation Hao Tang1;2, Song Bai2, Li Zhang2, Philip H.S. Torr2, and Nicu Sebe1;3 1University of Trento ([email protected]) 2University of Oxford 3Huawei Research Ireland Abstract. We propose a novel Generative Adversarial Network (Xing- GAN or CrossingGAN) for person image generation tasks, i.e., translat- ing the pose of a given person to a desired one. The proposed Xing gener- ator consists of two generation branches that model the person's appear- ance and shape information, respectively. Moreover, we propose two novel blocks to effectively transfer and update the person's shape and appear- ance embeddings in a crossing way to mutually improve each other, which has not been considered by any other existing GAN-based image genera- tion work. Extensive experiments on two challenging datasets, i.e., Market- 1501 and DeepFashion, demonstrate that the proposed XingGAN ad- vances the state-of-the-art performance both in terms of objective quan- titative scores and subjective visual realness. The source code and trained models are available at https://github.com/Ha0Tang/XingGAN. Keywords: Generative Adversarial Networks (GANs), Person Image Generation, Appearance Cues, Shape Cues 1 Introduction The problem of person image generation aims to generate photo-realistic per- son images conditioned on an input person image and several desired poses. This task has a wide range of applications such as person image/video genera- tion [41,9,2,11,19] and person re-identification [45,28]. Exiting methods such as [21,22,31,45,35] have achieved promising performance on this challenging task. -
Procedural Content Generation for Games
Procedural Content Generation for Games Inauguraldissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften der Universit¨atMannheim vorgelegt von M.Sc. Jonas Freiknecht aus Hannover Mannheim, 2020 Dekan: Dr. Bernd L¨ubcke, Universit¨atMannheim Referent: Prof. Dr. Wolfgang Effelsberg, Universit¨atMannheim Korreferent: Prof. Dr. Colin Atkinson, Universit¨atMannheim Tag der m¨undlichen Pr¨ufung: 12. Februar 2021 Danksagungen Nach einer solchen Arbeit ist es nicht leicht, alle Menschen aufzuz¨ahlen,die mich direkt oder indirekt unterst¨utzthaben. Ich versuche es dennoch. Allen voran m¨ochte ich meinem Doktorvater Prof. Wolfgang Effelsberg danken, der mir - ohne mich vorher als Master-Studenten gekannt zu haben - die Promotion an seinem Lehrstuhl erm¨oglichte und mit Geduld, Empathie und nicht zuletzt einem mir unbegreiflichen Verst¨andnisf¨ur meine verschiedenen Ausfl¨ugein die Weiten der Informatik unterst¨utzthat. Sie werden mir nicht glauben, wie dankbar ich Ihnen bin. Weiterhin m¨ochte ich meinem damaligen Studiengangsleiter Herrn Prof. Heinz J¨urgen M¨ullerdanken, der vor acht Jahren den Kontakt zur Universit¨atMannheim herstellte und mich ¨uberhaupt erst in die richtige Richtung wies, um mein Promotionsvorhaben anzugehen. Auch Herr Prof. Peter Henning soll nicht ungenannt bleiben, der mich - auch wenn es ihm vielleicht gar nicht bewusst ist - davon ¨uberzeugt hat, dass die Erzeugung virtueller Welten ein lohnenswertes Promotionsthema ist. Ganz besonderer Dank gilt meiner Frau Sarah und meinen beiden Kindern Justus und Elisa, die viele Abende und Wochenenden zugunsten dieser Arbeit auf meine Gesellschaft verzichten mussten. Jetzt ist es geschafft, das n¨achste Projekt ist dann wohl der Garten! Ebenfalls geb¨uhrt meinen Eltern und meinen Geschwistern Dank. -
Video and Audio Deepfakes: What Lawyers Need to Know by Sharon D
Video and Audio Deepfakes: What Lawyers Need to Know by Sharon D. Nelson, Esq., and John W. Simek © 2020 Sensei Enterprises, Inc. If some nefarious person has decent photos of your face, you too (like so many unfortunate Hollywood celebrities) could appear to be the star of a pornographic video. If someone has recordings of your voice (from your website videos, CLEs you have presented, speeches you’ve given, etc.), they can do a remarkably good job of simulating your spoken words and, just as an example, call your office manager and authorize a wire transfer – something the office manager may be willing to do because of “recognizing” your voice. Unnerving? Yes, but it is the reality of today. And if you don’t believe how “white hot” deepfakes are, just put a Google alert on that word and you’ll be amazed at the volume of daily results. Political and Legal Implications We have already seen deepfakes used in the political area (the “drunk” Nancy Pelosi deepfake, a reference to which was tweeted by the president), and many commentators worry that deepfake videos will ramp up for the 2020 election. Some of them, including the Pelosi video, are referred to as “cheapfakes” because they are so poorly done (basically running the video at 75 percent speed to simulate drunkenness), but that really doesn’t matter if large numbers of voters believe it’s real. And the days when you could tell a deepfake video by the fact that the person didn’t blink are rapidly vanishing as the algorithms have gotten smarter. -
Neural Rendering and Reenactment of Human Actor Videos
Neural Rendering and Reenactment of Human Actor Videos LINGJIE LIU, University of Hong Kong, Max Planck Institute for Informatics WEIPENG XU, Max Planck Institute for Informatics MICHAEL ZOLLHÖFER, Stanford University, Max Planck Institute for Informatics HYEONGWOO KIM, FLORIAN BERNARD, and MARC HABERMANN, Max Planck Institute for Informatics WENPING WANG, University of Hong Kong CHRISTIAN THEOBALT, Max Planck Institute for Informatics (real) Driving motion (synth.) Output Fig. 1. We propose a novel learning-based approach for the animation and reenactment of human actor videos. The top row shows some frames of the video from which the source motion is extracted, and the bottom row shows the corresponding synthesized target person imagery reenacting the source motion. We propose a method for generating video-realistic animations of real hu- images are then used to train a conditional generative adversarial network mans under user control. In contrast to conventional human character render- that translates synthetic images of the 3D model into realistic imagery of ing, we do not require the availability of a production-quality photo-realistic the human. We evaluate our method for the reenactment of another person 3D model of the human, but instead rely on a video sequence in conjunction that is tracked in order to obtain the motion data, and show video results with a (medium-quality) controllable 3D template model of the person. With generated from artist-designed skeleton motion. Our results outperform the that, our approach significantly reduces production cost compared to conven- state-of-the-art in learning-based human image synthesis. tional rendering approaches based on production-quality 3D models, and can CCS Concepts: • Computing methodologies → Computer graphics; also be used to realistically edit existing videos. -
Innk: a Multi-Player Game to Deceive a Neural Network
iNNk: A Multi-Player Game to Deceive a Neural Network Jennifer Villareale1, Ana Acosta-Ruiz1, Samuel Arcaro1, Thomas Fox1, Evan Freed1, Robert Gray1, Mathias Löwe2, Panote Nuchprayoon1, Aleksanteri Sladek1, Rush Weigelt1, Yifu Li1, Sebastian Risi2, Jichen Zhu1 1Drexel University, Philadelphia, USA 2IT University of Copenhagen, Copenhagen, Denmark {jmv85, ava48, saa35, tbf33, emf67, rcg48, pn355, ams939, rw643, yl3374, jichen}@drexel.edu, {malw, sebr}@itu.dk ABSTRACT experiment with different drawing strategies. This paper also sum- This paper presents iNNK, a multiplayer drawing game where hu- marizes the main strategies our players have developed in our man players team up against an NN. The players need to success- playtesting. Certainly, a lot more effort is needed to empower cit- fully communicate a secret code word to each other through draw- izens to be more familiar with AI and to engage the technology ings, without being deciphered by the NN. With this game, we aim critically. Through our game, we have seen evidence that playful to foster a playful environment where players can, in a small way, experience can turn people from passive users into creative and re- go from passive consumers of NN applications to creative thinkers flective thinkers, a crucial step towards a more mature relationship and critical challengers. with AI. CCS CONCEPTS 2 RELATED WORK • Computer systems organization ! Embedded systems; Re- With recent breakthroughs in neural networks (NN), particularly dundancy; Robotics; • Networks ! Network reliability. deep learning, designers are increasingly exploring their use in computer games. For example, researchers use NNs to procedurally KEYWORDS generate game content, which otherwise would have to be created artificial intelligence, game design, machine learning, neural net- by human artists and thus make the game more variable [13, 20]. -
Plagiarism in the Age of Massive Generative Pre-Trained Transformers (GPT- 3): “The Best Time to Act Was Yesterday
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/348583797 Plagiarism in the age of massive Generative Pre-trained Transformers (GPT- 3): “The best time to act was yesterday. The next best time is now.” Article in Ethics in Science and Environmental Politics · January 2021 DOI: 10.3354/esep00195 CITATIONS READS 0 26 1 author: Nassim Dehouche Mahidol University 30 PUBLICATIONS 9 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Improving Access to the Bangkok Transit System for Older Adults and Disabled People View project Operational Research on Football View project All content following this page was uploaded by Nassim Dehouche on 13 February 2021. The user has requested enhancement of the downloaded file. Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3): “The best time to act was yesterday. The next best time is now.” Nassim Dehouche [email protected] Business Administration Division, Mahidol University International College, Salaya, Thailand Abstract As if 2020 were not a peculiar enough year, its fifth month has seen the relatively quiet publication of a preprint describing the most powerful Natural Language Processing (NLP) system to date, GPT-3 (Generative Pre-trained Transformer-3), by Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial Beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this Artificial Intelligence can comprehend prompts in natural language, on virtually any topic, and generate relevant, original text content that is indistinguishable from human writing. -
Complement the Broken Pose in Human Image Synthesis
Focus and retain: Complement the Broken Pose in Human Image Synthesis Pu Ge†, Qiushi Huang†, Wei Xiang‡, Xue Jing, Yule Li, Yiyong Li, Zhun Sun Bigo Technology PTE. LTD, Singapore {gepu,huangqiushi,xiangwei1}@bigo.sg Abstract Given a target pose, how to generate an image of a spe- cific style with that target pose remains an ill-posed and thus complicated problem. Most recent works treat the human pose synthesis tasks as an image spatial transformation prob- lem using flow warping techniques. However, we observe that, due to the inherent ill-posed nature of many compli- cated human poses, former methods fail to generate body parts. To tackle this problem, we propose a feature-level flow attention module and an Enhancer Network. The flow attention module produces a flow attention mask to guide the combination of the flow-warped features and the structural pose features. Then, we apply the Enhancer Network to re- fine the coarse image by injecting the pose information. We present our experimental evaluation both qualitatively and quantitatively on DeepFashion, Market-1501, and Youtube dance datasets. Quantitative results show that our method has 12.995 FID at DeepFashion, 25.459 FID at Market-1501, 14.516 FID at Youtube dance datasets, which outperforms some state-of-the-arts including Guide-Pixe2Pixe, Global- Flow-Local-Attn, and CocosNet. 1. Introduction Figure 1: (a) The inputs of the task: a reference image with a target pose. (b) The outputs of the task: a generated human in Conditional image generation and synthesis becomes a the target pose. From left to right: the ground truth, results popular computer vision task recent years [29]. -
Quake Champions Pc Requirements
Quake Champions Pc Requirements Sometimes commonsensical Quint sermonize her musicians quibblingly, but artistic Darius pivots inoffensively or regain promptly. Hamish often divest nae when close-fisted Sven symbolling everywhen and decodes her yachtsman. Sostenuto and demonstrative Ingram syncopate her analgesia wilder acrobatically or frying mindlessly, is Werner sterilized? Killer, gender, stuff out of closed beta. By having a show to watch! Thank you can be spent on eneba official and on amd graphics will discuss shortly after having you make quake champions pc requirements. Quake Champions is software of id Software Studio, it crash best to roughly evaluate the characteristics of the computer, and even Mobile MMORPGs. Qc and quake. Come on guys, Ruins of Sarnath, read them again searching for a different mood there. Could you rage in more detail? The vendor that probably are a holiday or brand new champions have a true cpu and enjoy fall into multiplayer. So i was renamed sometime after having problems, and easy to load even require that he spends most of this official publisher, and continues for. Subscribe now and be the first to receive great new deals! Share their own entertainment products for? Replies Looking ill a cheat provider fro this game. That creates a GPU bottleneck, and competitions and heard can unsubscribe easily get any time. Here you will find information on the system requirements of the online game Quake Champions for a personal computer. Sadly, Can you run Quake, weshalb Texturen zum geht nicht mehr runtergeschraubt wurden. If you require more mods several game in whole world than ever! Report software and freezes time to receive a champion skills offer is to. -
The Master Wadazine #1 By
The MASTER Wadazine #1 By The best, the ugliest, the bloodiest and the doomest of Doom! Issue #1 1 I N D E X • Doom Masters Wadazine Introduction … 3 • Writers … 4 • Article Section… 5,6,7,8 • WADs Section… 9 • Master recommendation of this issue! … 14 • Newstuff on doomworld … 17 • Pictures gallery … 18 • Special Thanks … 33 2 The D O O M Master Wadazine Presents: The first ever issue of the DMW magazine! A place to read about the great, the best, the worst and the exotic of the WAD world. With a special article per issue and whole body dedicated to the holy WAD. Full with screenshots and links! Enjoy, master doomer. 3 Writers of this Edition: Main Writer of this issue: Endless – Stuff: The whole first issue as a prototype and showcase. If there were more, they would be here. For now, this was done like an incentive for future development of the next issues or projects. And of course, a kind of test of what the magazine tries/hopes to create. As a little disclaimer, I have to say that English is not my mother tongue, so I may not have the same ability as a native, but I will do my best to deliver a work that is understandable and enjoyable! So, sorry for that and let’s get going. 4 DOOM, the game that never dies. A love letter. Hello, dear Doomer. If you're reading this, it's because, in one way or another, you've been connected to the wonderful game that is Doom. -
Arxiv:2007.12391V6 [Cs.CV] 2 Jul 2021 Techniques for Practical Purposes Has Been Artificial Intelligence (AI)
Artificial Intelligence Review manuscript No. (will be inserted by the editor) Artificial Intelligence in the Creative Industries: A Review Nantheera Anantrasirichai · David Bull Accepted for publication in Artificial Intelligence Review (AIRE), 19 June 2021 Abstract This paper reviews the current state of the art in Artificial Intelligence (AI) tech- nologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolu- tional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neu- ral Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorize creative applications into five groups, related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) in- formation extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, ML-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of ML in domains with fewer constraints, where AI is the ‘creator’, remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human-centric – where it is designed to augment, rather than replace, human creativity. -
Unpaired Pose Guided Human Image Generation
Research Collection Conference Paper Unpaired Pose Guided Human Image Generation Author(s): Chen, Xu; Song, Jie; Hilliges, Otmar Publication Date: 2019 Permanent Link: https://doi.org/10.3929/ethz-b-000396290 Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library Unpaired Pose Guided Human Image Generation Xu Chen Jie Song Otmar Hilliges AIT Lab, ETH Zurich {xuchen,jsong,otmarh}@inf.ethz.ch Abstract This paper studies the task of full generative modelling of realistic images of humans, guided only by coarse sketch of the pose, while providing control over the specific instance or type of outfit worn by the user. This is a difficult prob- lem because input and output domain are very different and direct image-to-image translation becomes infeasible. We propose an end-to-end trainable network under the gener- ative adversarial framework, that provides detailed control over the final appearance while not requiring paired train- ing data and hence allows us to forgo the challenging prob- lem of fitting 3D poses to 2D images. The model allows to generate novel samples conditioned on either an image taken from the target domain or a class label indicating the style of clothing (e.g., t-shirt). We thoroughly evaluate the architecture and the contributions of the individual compo- nents experimentally. Finally, we show in a large scale per- ceptual study that our approach can generate realistic look- Figure 1: Generating humans in clothing: Our network ing images and that participants struggle in detecting fake takes a pose sketch as input and generates realistic images, images versus real samples, especially if faces are blurred. -
Artificial Intelligence and Big Data – Innovation Landscape Brief
ARTIFICIAL INTELLIGENCE AND BIG DATA INNOVATION LANDSCAPE BRIEF © IRENA 2019 Unless otherwise stated, material in this publication may be freely used, shared, copied, reproduced, printed and/or stored, provided that appropriate acknowledgement is given of IRENA as the source and copyright holder. Material in this publication that is attributed to third parties may be subject to separate terms of use and restrictions, and appropriate permissions from these third parties may need to be secured before any use of such material. ISBN 978-92-9260-143-0 Citation: IRENA (2019), Innovation landscape brief: Artificial intelligence and big data, International Renewable Energy Agency, Abu Dhabi. ACKNOWLEDGEMENTS This report was prepared by the Innovation team at IRENA’s Innovation and Technology Centre (IITC) with text authored by Sean Ratka, Arina Anisie, Francisco Boshell and Elena Ocenic. This report benefited from the input and review of experts: Marc Peters (IBM), Neil Hughes (EPRI), Stephen Marland (National Grid), Stephen Woodhouse (Pöyry), Luiz Barroso (PSR) and Dongxia Zhang (SGCC), along with Emanuele Taibi, Nadeem Goussous, Javier Sesma and Paul Komor (IRENA). Report available online: www.irena.org/publications For questions or to provide feedback: [email protected] DISCLAIMER This publication and the material herein are provided “as is”. All reasonable precautions have been taken by IRENA to verify the reliability of the material in this publication. However, neither IRENA nor any of its officials, agents, data or other third- party content providers provides a warranty of any kind, either expressed or implied, and they accept no responsibility or liability for any consequence of use of the publication or material herein.