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Towards Event-based Animation. By David Oziem Department of Computer Science. Many thanks to Colin Dalton, David Gibson and Neill Campbell. Introduction. Introduction. “A cheap costing for a photorealistic animated film is around £30,000 per minute”. Problem. Repeated actions.

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towards event based animation

Towards Event-based Animation

By David Oziem

Department of Computer Science

Many thanks to Colin Dalton, David Gibson and Neill Campbell

introduction
Introduction

SRCFE 2003: David Oziem, Dept. Computer Science

introduction1
Introduction

“A cheap costing for a photorealistic animated film is around £30,000 per minute”

SRCFE 2003: David Oziem, Dept. Computer Science

problem
Problem
  • Repeated actions.
  • The standard approach would be to repeat a single action many times.
    • Boring,
    • Unnatural,
    • Easily noticed.
  • Animating a long sequence would be hugely expensive and time consuming.

SRCFE 2003: David Oziem, Dept. Computer Science

auto regressive process
Auto-Regressive Process
  • Method of modelling sequences.
  • Learns patterns within a sequence, allowing new sequences to be generated.
  • Two potential media types:
    • Pixel values (Video clip)
    • Joint rotations (Animated character)

SRCFE 2003: David Oziem, Dept. Computer Science

campfire
Campfire
  • Example of a video texture.

Eigenpath & Original clip

SRCFE 2003: David Oziem, Dept. Computer Science

generated sequences
Generated Sequences

Plots of the motion through the eigenspace

SRCFE 2003: David Oziem, Dept. Computer Science

generated video clip
Generated Video Clip

Original Clip

Generated Sequence

SRCFE 2003: David Oziem, Dept. Computer Science

barman
Barman

SRCFE 2003: David Oziem, Dept. Computer Science

barman1
Barman
  • The barman.
    • Very short clip, only 200 frames,
    • Does not loop fluently.

SRCFE 2003: David Oziem, Dept. Computer Science

generated barman
Generated barman

Generated version of the barman scene

SRCFE 2003: David Oziem, Dept. Computer Science

horse gait states
Horse Gait (States)
  • Not all motion continually repeats itself in a Gaussian distribution,
  • Transitions between states may occur.

SRCFE 2003: David Oziem, Dept. Computer Science

crowd scene event driven
Crowd Scene (Event-driven)
  • State changes are driven by events.

A KLT (Kanade-Lucas-Tomasi) feature tracker was used to find the average vertical velocity in each of these 16 blocks.

SRCFE 2003: David Oziem, Dept. Computer Science

crowd scene sequence
Crowd scene (Sequence)

The first 3 modes against time

SRCFE 2003: David Oziem, Dept. Computer Science

crowd scene generation
Crowd scene (Generation)
  • An ARP creates an array of motions which are
    • Completely unlinked,
    • Not event driven.

SRCFE 2003: David Oziem, Dept. Computer Science

crowd scene generation1
Crowd scene (Generation)
  • The low frequency component used to dictate the general path of the primary mode.

SRCFE 2003: David Oziem, Dept. Computer Science

generated crowd
Generated crowd
  • Event occurs in all models at the same moment.
  • The crowd stands at different speeds and for varying durations.

SRCFE 2003: David Oziem, Dept. Computer Science

future work
Future work
  • Other forms of ARP’s. Including;
    • ARX Auto-Regression with eXternal signal.
    • TAR Transition Auto-Regression.
  • Use of Hidden Markov Models to drive state changes.

SRCFE 2003: David Oziem, Dept. Computer Science

summary
Summary
  • There are massive potential savings of generating sequences.
  • Natural motion is does NOT repeat perfectly.
  • Event driven state changes are difficult to model but it is possible.
  • ARP’s can generate new clips given that there is no change of state.

SRCFE 2003: David Oziem, Dept. Computer Science

questions
Questions?
  • oziem@cs.bris.ac.uk
  • http://www.cs.bris.ac.uk/home/oziem/

SRCFE 2003: David Oziem, Dept. Computer Science