Icon Learned Motion Matching

 

Icon Why Can't I Reproduce Their Results?

 

Icon Latinendian vs Arabendian

 

Icon Machine Learning, Kolmogorov Complexity, and Squishy Bunnies

 

Icon Subspace Neural Physics: Fast Data-Driven Interactive Simulation

 

Icon Software for Rent

 

Icon Naraleian Caterpillars

 

Icon The Scientific Method is a Virus

 

Icon Local Minima, Saddle Points, and Plateaus

 

Icon Robust Solving of Optical Motion Capture Data by Denoising

 

Icon Simple Concurrency in Python

 

Icon The Software Thief

 

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 RAGE

 

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 LOL I DREW THIS DRAGON

 

Icon One Cat Just Leads To Another

Learned Motion Matching

Created on Aug. 1, 2020, 3:30 p.m.

This year at SIGGRAPH I will be presenting Learned Motion Matching - a drop-in alternative to Motion Matching which scales to very large data sets. The basic idea consists of training three specialized neural networks to replace three specific components of the Motion Matching algorithm. Once trained, these networks can be used to create a controller which almost perfectly emulates the original Motion Matching system it was trained on, but which does not rely on keeping any animation data in memory. As Learned Motion Matching only requires Neural Network weights to be stored in memory it typically has much smaller memory usage, which remains small even as more and more data is added to the system.

WebpagePaperVideoSupplementary VideoArticle

Abstract: In this paper we present a learned alternative to the Motion Matching algorithm which retains the positive properties of Motion Matching but additionally achieves the scalability of neural-network-based generative models. Although neural-network-based generative models for character animation are capable of learning expressive, compact controllers from vast amounts of animation data, methods such as Motion Matching still remain a popular choice in the games industry due to their flexibility, predictability, low preprocessing time, and visual quality - all properties which can sometimes be difficult to achieve with neural-network-based methods. Yet, unlike neural networks, the memory usage of such methods generally scales linearly with the amount of data used, resulting in a constant trade-off between the diversity of animation which can be produced and real world production budgets. In this work we combine the benefits of both approaches and, by breaking down the Motion Matching algorithm into its individual steps, show how learned, scalable alternatives can be used to replace each operation in turn. Our final model has no need to store animation data or additional matching meta-data in memory, meaning it scales as well as existing generative models. At the same time, we preserve the behavior of Motion Matching, retaining the quality, control, and quick iteration time which are so important in the industry.

github twitter rss