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Haekwang Eom

dblp:334/7523 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2024
0000-0002-0158-3784ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Computer animation and physical simulation · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer animation and physical simulation
facial animation
0.612022
Animatomy: an Animator-centric, Anatomically Inspired System for 3D Facial Modeling, Animation and Transfer · SIGGRAPH Asia 2022
Computer animation and physical simulation › facial animation
facial expression transfer
0.612022
Animatomy: an Animator-centric, Anatomically Inspired System for 3D Facial Modeling, Animation and Transfer · SIGGRAPH Asia 2022

Methods — techniques the papers use, named apart from their topics

performance capture · 0.6muscle fiber curve representation · 0.6deformation architecture · 0.6
YearPublicationVenuePosition
2024 Interactive Locomotion Style Control for a Human Character based on Gait Cycle Features
abstract
Abstract This article introduces a data‐driven locomotion style controller for full‐body human characters using gait cycle features. Based on gait analysis, we define a set of gait features that can represent various locomotion styles as spatio‐temporal patterns within a single gait cycle. We compute the gait features for every single gait cycle in motion capture data and use them to search for the desired motion. Our real‐time style controller provides users with visual feedback for the changing inputs, exploiting the Motion Matching algorithm. We also provide a graphical controller interface that visualizes our style representation to enable intuitive control for users. We show that the proposed method is capable of retrieving appropriate locomotions for various gait cycle features, from simple walking motions to single‐foot motions such as hopping and dragging. To validate the effectiveness of our method, we conducted a user study that compares the usability and performance of our system with those of an existing footstep animation tool. The results show that our method is preferred over the baseline method for intuitive control and fast visual feedback.
Chaelin Kim, Haekwang Eom, Jung Eun Yoo, Soojin Choi, Jun-yong Noh
Comput. Graph. Forum2
2022 Animatomy: an Animator-centric, Anatomically Inspired System for 3D Facial Modeling, Animation and Transfer
abstract
We present Animatomy, a novel anatomic+animator centric representation of the human face. Present FACS-based systems are plagued with problems of face muscle separation, coverage, opposition, and redundancy. We, therefore, propose a collection of muscle fiber curves as an anatomic basis, whose contraction and relaxation provide us with a fine-grained parameterization of human facial expression. We build an end-to-end modular deformation architecture using this representation that enables: automatic optimization of the parameters of a specific face from high-quality dynamic facial scans; face animation driven by performance capture, keyframes, or dynamic simulation; interactive and direct manipulation of facial expression; and animation transfer from an actor to a character. We validate our facial system by showing compelling animated results, applications, and a quantitative comparison of our facial reconstruction to ground truth performance capture. Our system is being intensively used by a large creative team on Avatar: The Way of Water. We report feedback from these users as qualitative evaluation of our system.
Byungkuk Choi, Haekwang Eom, Benjamin Mouscadet, Stephen Cullingford, Wan-Duo Kurt Ma, Stefanie Gassel, Suzi Kim, Andrew Moffat, Millicent Maier, Marco Revelant, Joe Letteri, Karan Singh 0004
SIGGRAPH Asia2