• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Tuesday, August 26, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News

Translating skeletal movements, joint by joint

Bioengineer by Bioengineer
July 15, 2020
in Science News
Reading Time: 3 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

IMAGE

Credit: Kfir Aberman, Peizhuo Li, Dani Lischinski, Olga Sorkine-Hornung, Daniel Cohen-Or, Baoquan Chen

Every human body is unique, and the way in which a person’s body naturally moves depends on myriad factors, including height, weight, size, and overall shape. A global team of computer scientists has developed a novel deep-learning framework that automates the precise translation of human motion, specifically accounting for the wide array of skeletal structures and joints.

The end result? A seamless, much more flexible and universal framework for replicating human motion in the virtual world.

The team of researchers hail from AICFVE, the Beijing Film Academy, ETH Zurich, Hebrew University of Jerusalem, Peking University, and Tel Aviv University, and plan to demonstrate their work during SIGGRAPH 2020. The conference, which will take place virtually this year starting 17 August, gathers a network of leading professionals who approach computer graphics and interactive techniques from different perspectives. SIGGRAPH continues to serve as the industry’s premier venue for showcasing forward-thinking ideas and research. Registration for the virtual conference is now available.

Capturing the motion of humans remains a burgeoning and exciting field in computer animation and human-computer interaction. Motion capture (mocap) technology, particularly in filmmaking and visual effects, has made it possible to bring animated characters or digital actors to life. Mocap systems usually require the performer or actor to wear a set of markers or sensors that computationally capture their motions and 3D-skeleton poses. What remains a challenge in mocap is the ability to precisely transfer motion, also known as “motion retargeting,” between human skeletons, where the skeletons might differ in their structure depending on the number of bones and joints involved.

To date, mocap systems have not been successful in retargeting skeletons with different structures in a fully automated way. Errors are typically introduced in positions where joint correspondence cannot be specified. The team set out to address this specific problem and demonstrate that the framework can accurately replicate motion retargeting without specifying explicit pairing between the varying data sets.

“Our development is essential for using data from multiple mocap datasets that are captured with different systems within a single model,” Kfir Aberman, a senior author of the work and a researcher from AICFVE at the Beijing Film Academy, shared. “This enables the training of stronger, data-driven models that are setup-agnostic for various motion processing tasks.”

The team’s new motion processing framework contains special operators uniquely designed for motion data. The framework is general and can be used for various motion processing tasks. In particular, the researchers exploit its special properties to solve a practical problem in the mocap world, which makes their novel method widely applicable.

“I am particularly excited about the ability of our approach to encode motion into an abstract, skeleton-agnostic latent space,” Dani Lischinski, a coauthor of the work and professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, said. “A fascinating direction for future work would be to enable motion transfer between fundamentally different characters, such as bipeds and quadrupeds.”

In addition to Aberman and Lischinski, the collaborators on “Skeleton-aware Networks for Deep Motion Retargeting” include Peizhuo Li, Olga Sorkine-Hornung, Daniel Cohen-Or, and Baoquan Chen. The team’s paper and video can be found here and here.

###

Media Contact
Emily Drake
[email protected]

Original Source

https://arxiv.org/pdf/2005.05732.pdf

Tags: Computer ScienceMultimedia/Networking/Interface DesignTechnology/Engineering/Computer Science
Share12Tweet8Share2ShareShareShare2

Related Posts

Preeclampsia Alters Ferroptosis Markers in Placenta

August 26, 2025

Quantifying Age-Related Thymic Changes via Chest CT

August 26, 2025

N-Doped Carbon Coated SnP2O7 Enhances Lithium-Ion Anodes

August 26, 2025

Cardiac MRI’s Role in Pediatric Rosai-Dorfman Disease

August 26, 2025
Please login to join discussion

POPULAR NEWS

  • blank

    Breakthrough in Computer Hardware Advances Solves Complex Optimization Challenges

    147 shares
    Share 59 Tweet 37
  • Molecules in Focus: Capturing the Timeless Dance of Particles

    142 shares
    Share 57 Tweet 36
  • New Drug Formulation Transforms Intravenous Treatments into Rapid Injections

    115 shares
    Share 46 Tweet 29
  • Neuropsychiatric Risks Linked to COVID-19 Revealed

    81 shares
    Share 32 Tweet 20

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Preeclampsia Alters Ferroptosis Markers in Placenta

Quantifying Age-Related Thymic Changes via Chest CT

N-Doped Carbon Coated SnP2O7 Enhances Lithium-Ion Anodes

  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.