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echoinside
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ML in computer graphics and random stuff.
Any feedback: @fogside
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enjoying it means you are at right level, anxiety means you are fighting a difficult task. boredom is it's too easy for you and you need to make things difficult for yourself to advance
SimCLR

Abstract
SimCLR is a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework.
We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.

* Available in pytorch-lightning-bolts with a couple of code lines
* Paper
* Colab implementation
* Explanation and implementation details in a series of short videos
#self_supervised
https://poloclub.github.io/ganlab/
Милая интерактивная демка с объяснением ганов на tf.js.
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Flame plugin for blender for random face expression and random face generation
#morphable_model
https://github.com/HavenFeng/photometric_optimization

It seems here they optimize jointly mesh and texture from image with differential rendering, pytorch3d.
It takes ~20sec on 1 gpu GTX1080Ti to optimize for one image. Texture comes from PCA model which is more than 1Gb in size. It doesn't support well asian faces, because it optimizes only based on landmarks projected to 3d mesh.
But the results sometimes look quite promising.
#differentiable_rendering #face #face_reconstruction #morphable_model
​​NVidia released a technology to change face alignment on video

Nvidia has unveiled AI face-alignment that means you're always looking at the camera during video calls. Its new Maxine platform uses GANs to reconstruct the unseen parts of your head — just like a deepfake.

Link: https://www.theverge.com/2020/10/5/21502003/nvidia-ai-videoconferencing-maxine-platform-face-gaze-alignment-gans-compression-resolution

#NVidia #deepfake #GAN
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Найдено у CG_Vines.
Оригинальная линка на реддит.

https://ebsynth.com/
Если получится поиграть с приложением на макоси, дам знать.
Саму идею мы уже видели, а вот приложение еще нет.
Tutorial: https://youtu.be/0RLtHuu5jV4
echoinside
Найдено у CG_Vines. Оригинальная линка на реддит. https://ebsynth.com/ Если получится поиграть с приложением на макоси, дам знать. Саму идею мы уже видели, а вот приложение еще нет. Tutorial: https://youtu.be/0RLtHuu5jV4
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Попробовала сделать короткий видос с одним кейфреймом. Из важных особенностей — контуры на обрисованном видео и исходном видео должны совпадать. Нельзя пририсовать рожки, если их не было на видео или сделать нож из шаурмы.
картинка
Python 3.9 has been released.