File Download

There are no files associated with this item.

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)
Related Researcher

주경돈

Joo, Kyungdon
Robotics and Visual Intelligence Lab.
Read More

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

Full metadata record

DC Field Value Language
dc.citation.conferencePlace CN -
dc.citation.endPage 1931 -
dc.citation.startPage 1923 -
dc.citation.title AAAI Conference on Artificial Intelligence -
dc.contributor.author Gu, Dongjun -
dc.contributor.author Shim, Jaehyeok -
dc.contributor.author Jang, Jaehoon -
dc.contributor.author Kang, Changwoo -
dc.contributor.author Joo, Kyungdon -
dc.date.accessioned 2024-12-13T14:35:08Z -
dc.date.available 2024-12-13T14:35:08Z -
dc.date.created 2024-12-12 -
dc.date.issued 2024-02-24 -
dc.description.abstract Among various interactions between humans, such as eye contact and gestures, physical interactions by contact can act as an essential moment in understanding human behaviors. Inspired by this fact, given a 3D partner human with the desired interaction label, we introduce a new task of 3D human generation in terms of physical contact. Unlike previous works of interacting with static objects or scenes, a given partner human can have diverse poses and different contact regions according to the type of interaction. To handle this challenge, we propose a novel method of generating interactive 3D humans for a given partner human based on a guided diffusion framework (ContactGen in short). Specifically, we newly present a contact prediction module that adaptively estimates potential contact regions between two input humans according to the interaction label. Using the estimated potential contact regions as complementary guidances, we dynamically enforce ContactGen to generate interactive 3D humans for a given partner human within a guided diffusion model. We demonstrate ContactGen on the CHI3D dataset, where our method generates physically plausible and diverse poses compared to comparison methods. Source code is available at https://dongjunku.github.io/contactgen. -
dc.identifier.bibliographicCitation AAAI Conference on Artificial Intelligence, pp.1923 - 1931 -
dc.identifier.doi 10.1609/aaai.v38i3.27962 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/84815 -
dc.language 영어 -
dc.publisher Association for the Advancement of Artificial Intelligence -
dc.title ContactGen: Contact-Guided Interactive 3D Human Generation for Partners -
dc.type Conference Paper -
dc.date.conferenceDate 2024-02-20 -

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.