horsekrot.blogg.se - Conscious content creation workshop

#Conscious content creation workshop how to#
#Conscious content creation workshop Patch#
#Conscious content creation workshop full#

Yunjey Choi, Youngjung Uh (Clova AI Research, NAVER Corp.) Jaejun Yoo (EPFL) Jung-Woo Ha (Clova AI Research, NAVER Corp.)

#Conscious content creation workshop Patch#

RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval Hung-Yu Tseng, Hsin-Ying Lee (University of California, Merced) Lu Jiang (Google Research) Weilong Yang (Google Inc.) Ming-Hsuan Yang (University of California at Merced) Kuniaki Saito, Kate Saenko (Boston University) Ming-Yu Liu (NVIDIA)ĬOCO-FUNIT: Few-shot Unsupervised Image Translation with a Content-conditioned Style Encoder Text-guided Image Manipulation via Local Feature Editing Tianhao Zhang, Lu Jiang (Google Research) Weilong Yang (Google Inc.) Network Fusion for Content Creation with Conditional INNs Patrick Esser, Robin Rombach, Bjorn Ommer (Heidelberg University)

Learning to Synthesize Image and Video Contents SEAN: Image Synthesis with Semantic Region-adaptive Normalizationĭata-driven Graphic Design: Bringing AI into Graphic Design Peihao Zhu, Rameen Abdal (KAUST) Yipeng Qin (Cardiff University) Peter Wonka (KAUST)

#Conscious content creation workshop how to#

Image2StyleGAN++: How to Edit the Embedded Images?

Rameen Abdal, Peter Wonka (KAUST) Yipeng Qin (Cardiff University) Interpreting the Latent Space of GANs for Semantic Face Editing MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation Yuheng Li, Krishna Kumar Singh, Utkarsh Ojha, Yong Jae Lee (University of California, Davis) Sangwoo Mo (KAIST) Minsu Cho (POSTECH) Jinwoo Shin (KAIST)įreeze the Discriminator: A Simple Baseline for Fine-tuning GANs Towards High-quality Few-shot Font Generation with Dual Memory Attention Junbum Cha, Sanghyuk Chun, Gayoung Lee, Bado Lee, Seonghyeon Kim, Hwalsuk Lee (Clova AI Research, NAVER Corp.) SegAttnGAN: Text to Image Generation with Segmentation Attention Yuchuan Gou () Qiancheng Wu (University of California, Berkeley) Minghao Li, Bo Gong, Mei Han () Object-centric Image Generation from Layouts Tristan Sylvain (Mila) Pengchuan Zhang (Microsoft Research AI) Yoshua Bengio (Mila) R Devon Hjelm, Shikhar Sharma (Microsoft Research)

Generating 3D Content from 2D Supervision Deqing Sun, Ming-Yu Liu, Lu Jiang, James Tompkin, Weilong Yang, and Kalyan Sunkavalli. More broadly, we hope that the workshop will serve as a forum to discuss the latest topics in content creation and the challenges that vision and learning researchers can help solve.

Our invited designers will talk about the pain points that designers face using content creation tools.

To present selected success cases to advertise how deep learning can be used for content creation.

To cover some introductory concepts to help interested researchers from other fields get started in this exciting new area.

As such, the workshop is comprised of two parts: techniques for content creation and applications for content creation.

#Conscious content creation workshop full#

However, researchers and professionals in these fields may not be aware of its full potential and inner workings. Learned priors can also be combined with explicit geometric constraints, allowing for realistic and visually pleasing solutions to traditional problems such as novel view synthesis, in particular for the more complex cases of view extrapolation.ĪI for content creation lies at the intersection of the graphics, the computer vision, and the design community. Style transfer algorithms can convincingly render the content of one image with the style of another, offering unique opportunities for generating additional and more diverse training data-in addition to creating awe-inspiring, artistic images. Neural networks can create impressive and accurate slow-motion sequences from videos captured at standard frame rates, thus side-stepping the need for specialized and expensive hardware. For instance, generative adversarial networks (GANs) have been used to produce photorealistic images of items such as shoes, bags, and other articles of clothing, interior/industrial design, and even computer games' scenes. The recent progress of deep learning and machine learning techniques allowed to turn hours of manual, painstaking content creation work into minutes or seconds of automated work. Content creation has several important applications ranging from virtual reality, videography, gaming, and even retail and advertising. The AI for Content Creation workshop (AICCW) at CVPR 2020 brings together researchers in computer vision, machine learning, and AI.