Publications

Robust Solving of Optical Motion Capture Data by Denoising

ACM SIGGRAPH '18

Daniel Holden

In this research we present a method for computing the locations of a character's joints from optical motion capture marker data which is extremely robust to errors in the input, completely removing the need for any manual cleaning of the marker data. The core component of our method is a deep neural network which is trained to map from optical markers to joint positions and rotations. To make this neural network robust to errors in the input we train it on synthetic data, produced from a large database of skeletal motion capture data, where the marker locations have been reconstructed and then corrupted with a noise function designed to emulate the real kinds of errors that can appear in a typical optical motion capture setup.

Phase-Functioned Neural Networks for Character Control

ACM SIGGRAPH '17

Daniel Holden, Taku Komura, Jun Saito

Webpage • Paper • Slides • Video • Extras • Demo • Code & Data • Short 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.

Neural Network Ambient Occlusion

ACM SIGGRAPH Asia '16 Technical Briefs

Daniel Holden, Jun Saito, Taku Komura

Webpage • Paper • Video • Slides • Shader & Filters • Code & 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.

A Deep Learning Framework For Character Motion Synthesis and Editing

ACM SIGGRAPH '16

Daniel Holden, Jun Saito, Taku Komura

Webpage • Paper • Video • Slides • Code • Data

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.

Learning Motion Manifolds with Convolutional Autoencoders

ACM SIGGRAPH Asia '15 Technical Briefs

Daniel Holden, Jun Saito, Taku Komura, Thomas Joyce

Webpage • Paper • Video • Slides

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.

Learning an Inverse Rig Mapping for Character Animation

ACM SIGGRAPH/Eurographics SCA '15

Daniel Holden, Jun Saito, Taku Komura

Webpage • Paper • Video • Slides • Journal 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