Icon ASCII : A Love Letter


Icon My Neural Network isn't working! What should I do?


Icon Phase-Functioned Neural Networks for Character Control


Icon 17 Line Markov Chain


Icon 14 Character Random Number Generator


Icon Simple Two Joint IK


Icon Generating Icons with Pixel Sorting


Icon Neural Network Ambient Occlusion


Icon Three Short Stories about the East Coast Main Line


Icon The New Alphabet


Icon "The Color Munifni Exists"


Icon A Deep Learning Framework For Character Motion Synthesis and Editing


Icon The Halting Problem and The Moral Arbitrator


Icon The Witness


Icon Four Seasons Crisp Omelette


Icon At the Bottom of the Elevator


Icon Tracing Functions in Python


Icon Still Things and Moving Things


Icon water.cpp


Icon Making Poetry in Piet


Icon Learning Motion Manifolds with Convolutional Autoencoders


Icon Learning an Inverse Rig Mapping for Character Animation


Icon Infinity Doesn't Exist


Icon Polyconf


Icon Raleigh


Icon The Skagerrak


Icon Printing a Stack Trace with MinGW


Icon The Border Pines


Icon You could have invented Parser Combinators


Icon Ready for the Fight


Icon Earthbound


Icon Turing Drawings


Icon Lost Child Announcement


Icon Shelter


Icon Data Science, how hard can it be?


Icon Denki Furo


Icon In Defence of the Unitype


Icon Maya Velocity Node


Icon Sandy Denny


Icon What type of Machine is the C Preprocessor?


Icon Which AI is more human?


Icon Gone Home


Icon Thoughts on Japan


Icon Can Computers Think?


Icon Counting Sheep & Infinity


Icon How Nature Builds Computers


Icon Painkillers


Icon Correct Box Sphere Intersection


Icon Avoiding Shader Conditionals


Icon Writing Portable OpenGL


Icon The Only Cable Car in Ireland


Icon Is the C Preprocessor Turing Complete?


Icon The aesthetics of code


Icon Issues with SDL on iOS and Android


Icon How I learned to stop worrying and love statistics


Icon PyMark


Icon AutoC Tools


Icon Scripting xNormal with Python


Icon Six Myths About Ray Tracing


Icon The Web Giants Will Fall


Icon PyAutoC


Icon The Pirate Song


Icon Dear Esther


Icon Unsharp Anti Aliasing


Icon The First Boy


Icon Parallel programming isn't hard, optimisation is.


Icon Skyrim


Icon Recognizing a language is solving a problem


Icon Could an animal learn to program?




Icon Pure Depth SSAO


Icon Synchronized in Python


Icon 3d Printing


Icon Real Time Graphics is Virtual Reality


Icon Painting Style Renderer


Icon A very hard problem


Icon Indie Development vs Modding


Icon Corange


Icon 3ds Max PLY Exporter


Icon A Case for the Technical Artist


Icon Enums


Icon Scorpions have won evolution


Icon Dirt and Ashes


Icon Lazy Python


Icon Subdivision Modelling


Icon The Owl


Icon Mouse Traps


Icon Updated Art Reel


Icon Tech Reel


Icon Graphics Aren't the Enemy


Icon On Being A Games Artist


Icon The Bluebird


Icon Everything2


Icon Duck Engine


Icon Boarding Preview


Icon Sailing Preview


Icon Exodus Village Flyover


Icon Art Reel




Icon One Cat Just Leads To Another


publication pfnn

Phase-Functioned Neural Networks for Character Control


Daniel Holden, Taku Komura, Jun Saito

WebpagePaperSlidesVideoExtrasDemo Code & DataShort Talk (15 mins)

This paper uses a new kind of neural network called a "Phase-Functioned Neural Network" to produce a character controller for games which generates high quality motion, requires very little memory, is very fast to compute, and can be used in complex and difficult environments such as traversing rough terrain.

publication nnao

Neural Network Ambient Occlusion

ACM SIGGRAPH Asia '16 Technical Briefs

Daniel Holden, Jun Saito, Taku Komura

WebpagePaperVideoSlidesShader & FiltersCode & Data

This short paper uses Machine Learning to produce ambient occlusion from the screen space depth and normals. A large database of ambient occlusion is rendered offline and a neural network trained to produce ambient occlusion from a small patch of screen space information. This network is then converted into a fast runtime shader that runs in a single pass and can be used as a drop-in replacement to other screen space ambient occlusion techniques.

publication synthesis

A Deep Learning Framework For Character Motion Synthesis and Editing


Daniel Holden, Jun Saito, Taku Komura


In this work we show how to apply deep learning techniques to character animation data.

We present a number of applications, including very fast motion synthesis, natural motion editing, and style transfer - and describe the potential for future applications and work. Unlike previous methods our technique requires no manual preprocessing of the data, instead learning as much as possible unsupervised.

publication manifold

Learning Motion Manifolds with Convolutional Autoencoders

ACM SIGGRAPH Asia '15 Technical Briefs

Daniel Holden, Jun Saito, Taku Komura, Thomas Joyce


In this work we show how a motion manifold can be constructed using deep convolutional autoencoders.

Once constructed the motion manifold has many uses in animation research and machine learning. It can be used to fix corrupted motion data, fill in missing motion data, and naturally interpolate or take the distance between different motions.

publication rigmapping

Learning an Inverse Rig Mapping for Character Animation

ACM SIGGRAPH/Eurographics SCA '15

Daniel Holden, Jun Saito, Taku Komura

WebpagePaperVideoSlidesJournal Paper

In this work we present a technique for mapping skeletal joint points, such as those found via motion capture onto rig controls, the controls used by animators in keyframed animation environments.

This technique performs the mapping in real-time allowing for the seamless integration of artistic tools that work in the space of the joint positions to be used by key-framing artists - a big step torward the application of many existing animation tools for character animation.

Other Publications

Fast Neural Style Transfer for Motion Data

IEEE Computer Graphics and Applications '17 • Daniel Holden, Ikhsanul Habibie, Taku Komura, Ikuo Kusajima

Carpet unrolling for character control on uneven terrain

ACM SIGGRAPH/Eurographics MIG '15 • Mark Miller, Daniel Holden, Rami Al-Ashqar, Christophe Dubach, Kenny Mitchell, Taku Komura

A Recurrent Variational Autoencoder for Human Motion Synthesis

British Machine Vision Conference '17 • Ikhsanul Habibie, Daniel Holden, Jonathan Schwarz, Joe Yearsley, Taku Komura

Scanning and animating characters dressed in multiple-layer garments

The Visual Computer 2017 • Pengpeng Hu, Taku Komura, Daniel Holden, Yueqi Zhong

github twitter rss