Towards a Virtual Stuntmanrail.eecs.berkeley.edu/deeprlcourse-fa18/static/slides/...UC Berkeley...

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Towards a Virtual Stuntman Xue Bin (Jason) Peng UC Berkeley

Transcript of Towards a Virtual Stuntmanrail.eecs.berkeley.edu/deeprlcourse-fa18/static/slides/...UC Berkeley...

  • Towards a Virtual Stuntman

    Xue Bin (Jason) Peng UC Berkeley

  • Animation

  • Animation

  • Computer Animation

    [Ju et al. 2013] [Brown et al. 2013]

    [Tan et al. 2014]

    [Geijtenbeek et al. 2013]

    [Kwon and Hodgins 2017] [Peng et al. 2018]

  • Physics-Based Animation

    [Coros et al. 2011]

  • Deep RL

    [Schulman et al. 2016] [Mnih et al. 2015] [Chebotar et al. 2017]

  • Motion Quality

    [Heess et al. 2017] [Schulman et al. 2016]

  • Motivation

  • Which is Mocap?

  • Simulation Mocap

    Which is Mocap?

  • Overview

    + +

    Character Reference Motion Task: Hit Target

  • Overview

  • Overview

  • Overview

  • Reference Motion

  • Reference Motion

  • Reference Motion

  • Reference Motion

  • Reference Motion

  • Reference Motion

  • Reference Motion

    đť‘Ž0 đť‘Ž1 đť‘Ž2 đť‘Ž3 đť‘Ž4

  • State + Action

    State:

    • link positions

    • link velocities

  • State + Action

    State:

    • link positions

    • link velocities

  • State + Action

    State:

    • link positions

    • link velocities

    Action:

    • PD targets

  • State + Action

    State:

    • link positions

    • link velocities

    Action:

    • PD targets

  • Reward

  • Reward

    Imitation Objective

  • Reward

    Imitation Objective

  • Reward

    Imitation Objective

  • Reward

    Imitation Objective

  • Reward

    Imitation Objective Task Objective

  • Proximal Policy Optimization (PPO)

    [Schulman et al. 2017]

  • Proximal Policy Optimization (PPO)

    [Schulman et al. 2017]

  • Proximal Policy Optimization (PPO)

    [Schulman et al. 2017]

  • Proximal Policy Optimization (PPO)

    [Schulman et al. 2017]

  • Proximal Policy Optimization (PPO)

    [Schulman et al. 2017]

  • Humanoid: Walk

  • Humanoid: Run

  • Comparison

    [Merel et al. 2017] Ours

  • No Reference Motion

  • Locomotion

  • Humanoid: Cartwheel

  • Humanoid: Backflip

  • Humanoid: Frontflip

  • Humanoid: Roll

  • Humanoid: Crawl

  • Humanoid: Dance A

  • Humanoid: Kip-Up

  • Humanoid: Headspin

  • Humanoid: Vault 1-Handed

  • Humanoid: Flare

  • 20+ Skills

  • Keyframe Animation

  • T-Rex: Walk

    Simulated Character

  • Dragon: Walk

    Simulated Character

  • Lion: Run

    Simulated Character

  • Lion++

  • Tasks

  • Tasks

    Reference Motion

  • Tasks

    Reference Motion

    +

    Task

  • Humanoid: Spinkick - Strike

  • Humanoid: Baseball Pitch - Throw

  • No Reference Motion

  • Humanoid: Balance Beam

  • Humanoid: Run – Dense Gaps

  • Retargeting

  • Character Retargeting

    +

    Reference Motion Atlas

  • Atlas: Spinkick

  • Atlas: Run

  • Atlas: Getup-Facedown

  • Atlas: Backflip

  • Multi-Clip Integration

  • Multi-Clip Integration

  • Multi-Clip Integration

  • Learning from Mocap

  • Mocap is a Hassle

    [Holden 2018]

  • Skills From Videos

  • Simulation

    Learning from Videos

    Video Simulation

  • Overview

  • Overview

  • Overview

  • Overview

  • Overview

  • Pose Estimation

  • Pose Estimation

    Pose Prediction Video: Handspring A

  • Human Mesh Recovery (HMR)

    [Kanazawa et al., 2018]

  • Pose Estimation

    Pose Prediction Video: Backflip A

  • Motion Imitation

  • Motion Imitation

  • Motion Imitation

  • Motion Imitation

  • Humanoid: Cartwheel B

    Video: Cartwheel B Reference Motion Policy

  • Humanoid: Jumping Jack

    Video: Jumping Jack Policy

  • Humanoid: Backflip B

    Video: Backflip B Policy

  • Humanoid: Frontflip

    Video: Frontflip Policy

  • Humanoid: Roll

    Video: Roll Policy

  • Humanoid: Spin

    Video: Spin Policy

  • Humanoid: Kip-Up

    Video: Kip-Up Policy

  • Humanoid: Vault

    Video: Vault Policy

  • Atlas: Handspring A

    Video: Handspring A Policy

  • Atlas: Jump

    Video: Jump Policy

  • Atlas: Vault

    Video: Vault Policy

  • Atlas: Dance

    Video: Dance Policy

  • Environment Retargeting

  • Environment Retargeting

  • Failure Cases

  • Skills from Videos

    Policy Video: Backflip A

  • Concluding Remarks

    • Simple method can learn a large repertoire of skills

    • Minimizing tracking error works (surprisingly) well

  • Concluding Remarks

    • Simple method can learn a large repertoire of skills

    • Minimizing tracking error works (surprisingly) well

    • A lot of room for improvement for video imitation – More end-to-end approach

    – Outdoor sports

    – Multiple actors

  • Concluding Remarks

    • Simple method can learn a large repertoire of skills

    • Minimizing tracking error works (surprisingly) well

    • A lot of room for improvement for video imitation – More end-to-end approach

    – Outdoor sports

    – Multiple actors

    • Code: https://github.com/xbpeng/DeepMimic

  • Collaborators

    Pieter Abbeel Sergey Levine Michiel van de Panne Angjoo Kanazawa Jitendra Malik

  • Questions?


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