The document discusses advanced rendering techniques for achieving anime-style rendering in Unity. It covers topics such as advanced rendering features for mobile like bloom and dynamic particles. It also discusses stylized scene lighting, specialized shaders for materials like silk and hair, character shading techniques including multi-ramp shading and facial expression blending, and effects like volume lights, depth of field, and image-space outlines. The overall goal is to achieve high quality anime-style rendering on mobile and PC platforms.
The document discusses advanced rendering techniques for achieving anime-style rendering in Unity. It covers topics such as advanced rendering features for mobile like bloom and dynamic particles. It also discusses stylized scene lighting, specialized shaders for materials like silk and hair, character shading techniques including multi-ramp shading and facial expression blending, and effects like volume lights, depth of field, and image-space outlines. The overall goal is to achieve high quality anime-style rendering on mobile and PC platforms.
UE4のライトビルドシステムであるライトマスの設定が内部アルゴリズムにどのように影響するかをイラストにてなるべくかみ砕いて説明しております。
内部アルゴリズムの詳しい挙動は本公演のVol1を参考にしてください。
https://www.slideshare.net/EpicGamesJapan/lightmass-deep-dive-2018-vol1-lightmasslightmap
※こちらは2016年に行った"Lightmass Deep Dive"の2018年度版になります。
Original Slide: https://www.slideshare.net/EpicGamesJapan/lightmass-lightmap-epic-games-japan
(Epic Games Japan: 篠山範明)
This document provides an overview of high dynamic range (HDR) technology and workflows for HDR video production and mastering. It discusses HDR standards like SMPTE ST 2084 and ARIB STB-B67, camera log curves, luminance levels, and tools for setting up HDR monitoring including waveform monitors. Specific topics covered include HDR graticules, setting luminance levels for highlights and grey points, and using zebra patterns and zoom modes to evaluate highlight levels in HDR images.
This document discusses techniques for lighting and tonemapping in 3D graphics to better simulate the human visual system. It covers gamma correction, which accounts for how monitors display light intensities non-linearly. It also discusses filmic tonemapping, which produces crisp blacks, saturated dark tones, and soft highlights similar to film, by applying a tone curve modeled after photographic film. This provides advantages over other tonemapping operators like Reinhard for reproducing accurate colors across a high dynamic range.
The document discusses screen space reflections implemented in the game The Surge. It describes using screen space ray marching against the depth buffer to find reflection points, convolving the scene to accumulate multiple bounces, and using asynchronous compute to overlap rendering passes and improve performance. Key techniques included interleaved rendering, temporal reprojection, and using local data storage. Performance gains were achieved through optimizations like lower resolution rendering and computing mip chains in-place.
Physically Based Lighting in Unreal Engine 4Lukas Lang
Talk held at Unreal Meetup Munich on 15th May 2019.
I talked about some of the theoretical background of physically based lighting, demonstrated a workflow + containing value tables needed to be able to easily use the workflow.
UE4は4.19からInput Latencyの改善を行える設定が加わりました。
https://docs.unrealengine.com/ja/Platforms/LowLatencyFrameSyncing/index.html
その設定が実際どのようなことをしているのか質問されることが多かったため、今回簡単にですがドキュメトにまとめてみました。各スレッドの並列動作を理解する必要があり事前説明がちょいと長いのですが、ご参考になれば幸いです。
(Epic Games Japan Support Manager 篠山範明)
講演動画はこちら:
https://youtu.be/GEl8AfgI35g
講演者:
小林 浩之(Epic Games Japan)
https://twitter.com/hannover_bloss
本スライドは2021年7月25日に行われたオンライン勉強会「UE4 Character Art Dive Online」の講演資料となります。
イベントについてはこちら:
https://www.unrealengine.com/ja/blog/epicgamesjapan-onlinelearning-13
UE4のライトビルドシステムであるライトマスの設定が内部アルゴリズムにどのように影響するかをイラストにてなるべくかみ砕いて説明しております。
内部アルゴリズムの詳しい挙動は本公演のVol1を参考にしてください。
https://www.slideshare.net/EpicGamesJapan/lightmass-deep-dive-2018-vol1-lightmasslightmap
※こちらは2016年に行った"Lightmass Deep Dive"の2018年度版になります。
Original Slide: https://www.slideshare.net/EpicGamesJapan/lightmass-lightmap-epic-games-japan
(Epic Games Japan: 篠山範明)
This document provides an overview of high dynamic range (HDR) technology and workflows for HDR video production and mastering. It discusses HDR standards like SMPTE ST 2084 and ARIB STB-B67, camera log curves, luminance levels, and tools for setting up HDR monitoring including waveform monitors. Specific topics covered include HDR graticules, setting luminance levels for highlights and grey points, and using zebra patterns and zoom modes to evaluate highlight levels in HDR images.
This document discusses techniques for lighting and tonemapping in 3D graphics to better simulate the human visual system. It covers gamma correction, which accounts for how monitors display light intensities non-linearly. It also discusses filmic tonemapping, which produces crisp blacks, saturated dark tones, and soft highlights similar to film, by applying a tone curve modeled after photographic film. This provides advantages over other tonemapping operators like Reinhard for reproducing accurate colors across a high dynamic range.
The document discusses screen space reflections implemented in the game The Surge. It describes using screen space ray marching against the depth buffer to find reflection points, convolving the scene to accumulate multiple bounces, and using asynchronous compute to overlap rendering passes and improve performance. Key techniques included interleaved rendering, temporal reprojection, and using local data storage. Performance gains were achieved through optimizations like lower resolution rendering and computing mip chains in-place.
Physically Based Lighting in Unreal Engine 4Lukas Lang
Talk held at Unreal Meetup Munich on 15th May 2019.
I talked about some of the theoretical background of physically based lighting, demonstrated a workflow + containing value tables needed to be able to easily use the workflow.
UE4は4.19からInput Latencyの改善を行える設定が加わりました。
https://docs.unrealengine.com/ja/Platforms/LowLatencyFrameSyncing/index.html
その設定が実際どのようなことをしているのか質問されることが多かったため、今回簡単にですがドキュメトにまとめてみました。各スレッドの並列動作を理解する必要があり事前説明がちょいと長いのですが、ご参考になれば幸いです。
(Epic Games Japan Support Manager 篠山範明)
講演動画はこちら:
https://youtu.be/GEl8AfgI35g
講演者:
小林 浩之(Epic Games Japan)
https://twitter.com/hannover_bloss
本スライドは2021年7月25日に行われたオンライン勉強会「UE4 Character Art Dive Online」の講演資料となります。
イベントについてはこちら:
https://www.unrealengine.com/ja/blog/epicgamesjapan-onlinelearning-13
Subtle Anamorphic Lens Effects - Real-time Rendering of Physically Based Opt...Silicon Studio Corporation
These slides are a portion of the lecture on "Real-Time Rendering of Physically Based Optical Effects in Theory and Practice" at Siggraph 2015. The whole course is available on the tri-Ace web site. All of the Silicon Studio slides are available from our web site.
Silicon Studio: http://www.siliconstudio.co.jp/rd/presentations/
tri-Ace: http://research.tri-ace.com/s2015.html
このスライドはSIGGRAPH2015のCourse「Real-Time Rendering of Physically Based Optical Effects in Theory and Practice」の講演資料の一部です。Course全体のスライドはトライエースのWebサイトに掲載されています。元のスライドデータは、シリコンスタジオまたはトライエースのWebサイトからダウンロードできます。
シリコンスタジオ:http://www.siliconstudio.co.jp/rd/presentations/
トライエース:http://research.tri-ace.com/s2015.html
Recipes for Optical Effect System Design - Real-time Rendering of Physically...Silicon Studio Corporation
This document provides guidance on parameters for simulating optical effects in a physically plausible way. It discusses using parameters based on optics and photography to avoid unnatural results. Key topics covered include handling depth of field, focus breathing, variable maximum apertures of zoom lenses, and the law of reciprocity in optics. The document recommends automatically controlling parameters that vary due to lens mechanisms and selectively applying the law of reciprocity based on objectives.
Lenses - Real-time Rendering of Physically Based Optical Effect in Theory an...Silicon Studio Corporation
These slides are a portion of the lecture on "Real-Time Rendering of Physically Based Optical Effects in Theory and Practice" at Siggraph 2015. The whole course is available on the tri-Ace web site. All of the Silicon Studio slides are available from our web site.
Silicon Studio: http://www.siliconstudio.co.jp/rd/presentations/
tri-Ace: http://research.tri-ace.com/s2015.html
このスライドはSIGGRAPH2015のCourse「Real-Time Rendering of Physically Based Optical Effects in Theory and Practice」の講演資料の一部です。Course全体のスライドはトライエースのWebサイトに掲載されています。元のスライドデータは、シリコンスタジオまたはトライエースのWebサイトからダウンロードできます。
シリコンスタジオ:http://www.siliconstudio.co.jp/rd/presentations/
トライエース:http://research.tri-ace.com/s2015.html
Making Your Bokeh Fascinating - Real-time Rendering of Physically Based Opti...Silicon Studio Corporation
These slides are a portion of the lecture on "Real-Time Rendering of Physically Based Optical Effects in Theory and Practice" at Siggraph 2015. The whole course is available on the tri-Ace web site. All of the Silicon Studio slides are available from our web site.
Silicon Studio: http://www.siliconstudio.co.jp/rd/presentations/
tri-Ace: http://research.tri-ace.com/s2015.html
このスライドはSIGGRAPH2015のCourse「Real-Time Rendering of Physically Based Optical Effects in Theory and Practice」の講演資料の一部です。Course全体のスライドはトライエースのWebサイトに掲載されています。元のスライドデータは、シリコンスタジオまたはトライエースのWebサイトからダウンロードできます。
シリコンスタジオ:http://www.siliconstudio.co.jp/rd/presentations/
トライエース:http://research.tri-ace.com/s2015.html
In mobile social games, reducing user attrition, i.e. churn, is decisive to increase player retention and rise revenues. Survival analysis focuses on predicting the time of occurrence of a certain event and efficiently deals with the censored data problem, which is in the nature of churn. However, due to the inflexibility of the traditional survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results.
In this talk, we present a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn for each player, as a function of time and game level. Firstly, we describe that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses. Secondly, we discuss these results in the framework of Game Data Science as a Service. The goal of Silicon Studio is to Democratize Game Data Science. Hence, the proposed method is able to make predictions in an operational business environment and easily adapts to different kinds of games, players, and therefore distributions of the data. We focus on a flexible technique that does not need a previous manipulation of the data and that is able to deal efficiently with the temporal dimension of the churn prediction problem.
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using ...Silicon Studio Corporation
Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results. In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression.