シェーダーを活用した3Dライブ演出のアップデート ~『ラブライブ!スクールアイドルフェスティバル ALL STARS』(スクスタ)の開発事例~?KLab Inc. / Tech
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This document discusses updates to 3D live performance rendering in Love Live! School Idol Festival All Stars (SIFAS) using shaders. It describes how vertex shaders were used to animate butterfly wings flapping and fans waving to reduce CPU load while maintaining production efficiency. Particle systems were combined with custom vertex streams and shader modifications to extend the single butterfly implementation to multiple butterflies. GPU instancing was also proposed as an alternative solution.
【DL輪読会】NeRF-VAE: A Geometry Aware 3D Scene Generative ModelDeep Learning JP
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NeRF-VAE is a 3D scene generative model that combines Neural Radiance Fields (NeRF) and Generative Query Networks (GQN) with a variational autoencoder (VAE). It uses a NeRF decoder to generate novel views conditioned on a latent code. An encoder extracts latent codes from input views. During training, it maximizes the evidence lower bound to learn the latent space of scenes and allow for novel view synthesis. NeRF-VAE aims to generate photorealistic novel views of scenes by leveraging NeRF's view synthesis abilities within a generative model framework.
This document discusses Yarn and its advantages over npm. It notes that Yarn uses yarn.lock files instead of npm-shrinkwrap.json files to lock down dependency versions. Yarn is also described as being faster, able to work offline by caching dependencies, and potentially more secure than npm with features like flat mode and module folders. The document suggests Yarn may handle dependencies and devDependencies differently than npm, and questions whether the yarn.lock file should be committed to source control.
シェーダーを活用した3Dライブ演出のアップデート ~『ラブライブ!スクールアイドルフェスティバル ALL STARS』(スクスタ)の開発事例~?KLab Inc. / Tech
?
This document discusses updates to 3D live performance rendering in Love Live! School Idol Festival All Stars (SIFAS) using shaders. It describes how vertex shaders were used to animate butterfly wings flapping and fans waving to reduce CPU load while maintaining production efficiency. Particle systems were combined with custom vertex streams and shader modifications to extend the single butterfly implementation to multiple butterflies. GPU instancing was also proposed as an alternative solution.
【DL輪読会】NeRF-VAE: A Geometry Aware 3D Scene Generative ModelDeep Learning JP
?
NeRF-VAE is a 3D scene generative model that combines Neural Radiance Fields (NeRF) and Generative Query Networks (GQN) with a variational autoencoder (VAE). It uses a NeRF decoder to generate novel views conditioned on a latent code. An encoder extracts latent codes from input views. During training, it maximizes the evidence lower bound to learn the latent space of scenes and allow for novel view synthesis. NeRF-VAE aims to generate photorealistic novel views of scenes by leveraging NeRF's view synthesis abilities within a generative model framework.
This document discusses Yarn and its advantages over npm. It notes that Yarn uses yarn.lock files instead of npm-shrinkwrap.json files to lock down dependency versions. Yarn is also described as being faster, able to work offline by caching dependencies, and potentially more secure than npm with features like flat mode and module folders. The document suggests Yarn may handle dependencies and devDependencies differently than npm, and questions whether the yarn.lock file should be committed to source control.