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Kim Ji-Yeon

dblp:393/6967 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0002-7535-380XORCID · corroborated

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

Artificial intelligence and machine learning · 2 · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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.

Artificial intelligence
1 paper
Generative modeling · 100%
Computer graphics and multimedia
1 paper
Visual content generation and editing · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling › diffusion model
human motion generation
1.012026
CLIP-Actor-X: Text-Driven 4D Human Avatar Generation via Cross-Modal Synthesis-Through-Optimization · IEEE Trans. Pattern Anal. Mach. Intell. 2026
Machine learning › Generative modeling › motion generation
text-driven motion generation
1.012026
CLIP-Actor-X: Text-Driven 4D Human Avatar Generation via Cross-Modal Synthesis-Through-Optimization · IEEE Trans. Pattern Anal. Mach. Intell. 2026

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

text-to-motion diffusion model · 2.0retrieval-augmented generation · 2.0neural style optimization · 2.0
YearPublicationVenuePosition
2026 CLIP-Actor-X: Text-Driven 4D Human Avatar Generation via Cross-Modal Synthesis-Through-Optimization
abstract
We propose CLIP-Actor-X, a text-driven motion generation and neural mesh stylization system for 4D human avatar generation. CLIP-Actor-X generates a detailed 3D human mesh, motion animation, and texture to conform to a given text prompt input from a user. CLIP- Actor-X system mainly consists of two modules. First, for generating realistic human motion, we build a text-driven human motion synthesis module modeled by a retrieval-augmented generative model, powered by a text-to-motion diffusion model. Second, our novel zero-shot neural style optimization module detailizes and texturizes the sampled sequence of a neutral human mesh template, such that the resulting mesh and appearance comply with the input text prompt in a temporally-consistent and pose-agnostic manner. In contrast to the prior arts that use an artist-designed, non-animatable mesh as an input, our output representation is animatable and better aligned between an input text and the generated avatar without additional post-processes, e.g., re-alignment, retargeting, or rigging. We further propose the ways to stabilize the optimization process: spatio-temporal view augmentation and visibility-aware embedding attention, which deals with poorly rendered views. We demonstrate that CLIP-Actor-X produces perceptually plausible and human-recognizable human avatar in motion with detailed geometry and texture solely from a natural language prompt.
Kim Youwang, Taehyun Byun, Kim Ji-Yeon, Tae-Hyun Oh
IEEE Trans. Pattern Anal. Mach. Intell.3
2024 The Devil Is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning
Wonjun Jo, Byung-Ki Kwon, Kim Ji-Yeon, Hawook Jeong, Kyungdon Joo, Tae-Hyun Oh
ACCV (9)3