36 citations · 76 across the 19 of their papers we have counts for
19 papers
Grounding World Simulation Models in a Real-World Metropolis
Junyoung Seo, Hyunwook Choi, Minkyung Kwon +10
What if a world simulation model could render not an imagined environment but a city that actually exists? Prior generative world models synthesize visually plausible yet artificia…
MoECLIP: Patch-Specialized Experts for Zero-shot Anomaly Detection
Jun Yeong Park, JunYoung Seo, Minji Kang +1
The CLIP model's outstanding generalization has driven recent success in Zero-Shot Anomaly Detection (ZSAD) for detecting anomalies in unseen categories. The core challenge in ZSAD…
Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting
Seunghan Lee, Jaehoon Lee, Jun Seo +7
Retrieval-augmented generation (RAG) enhances zero-shot time series (TS) forecasting by leveraging external knowledge bases, yet existing approaches overlook input-level relevance…
CAMEO: Correspondence-Attention Alignment for Multi-View Diffusion Models
Minkyung Kwon, Jinhyeok Choi, Jiho Park +6
Multi-view diffusion models have recently emerged as a powerful paradigm for novel view synthesis, yet the underlying mechanism that enables their view-consistency remains unclear.…
Lookahead Anchoring: Preserving Character Identity in Audio-Driven Human Animation
Junyoung Seo, Rodrigo Mira, Alexandros Haliassos +6
Audio-driven human animation models often suffer from identity drift during temporal autoregressive generation, where characters gradually lose their identity over time. One soluti…
3D Scene Prompting for Scene-Consistent Camera-Controllable Video Generation
JoungBin Lee, Jaewoo Jung, Jisang Han +6
We present 3DScenePrompt, a framework that generates the next video chunk from arbitrary-length input while enabling precise camera control and preserving scene consistency. Unlike…
Vid-CamEdit: Video Camera Trajectory Editing with Generative Rendering from Estimated Geometry
Junyoung Seo, Jisang Han, Jaewoo Jung +9
We introduce Vid-CamEdit, a novel framework for video camera trajectory editing, enabling the re-synthesis of monocular videos along user-defined camera paths. This task is challen…
DUSt3R: Enhancing 3D Reconstruction for Dynamic Scenes
Jisang Han, Honggyu An, Jaewoo Jung +7
In this work, we address the task of 3D reconstruction in dynamic scenes, where object motions frequently degrade the quality of previous 3D pointmap regression methods, such as DU…
Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification
Inès Hyeonsu Kim, Woojeong Jin, Soowon Son +6
Person re-identification (Re-ID) often faces challenges due to variations in human poses and camera viewpoints, which significantly affect the appearance of individuals across imag…
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping
Junyoung Seo, Kazumi Fukuda, Takashi Shibuya +6
Generating novel views from a single image remains a challenging task due to the complexity of 3D scenes and the limited diversity in the existing multi-view datasets to train a mo…
Retrieval-Augmented Score Distillation for Text-to-3D Generation
Junyoung Seo, Susung Hong, Wooseok Jang +4
Text-to-3D generation has achieved significant success by incorporating powerful 2D diffusion models, but insufficient 3D prior knowledge also leads to the inconsistency of 3D geom…
DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation
Susung Hong, Junyoung Seo, Heeseong Shin +2
In the paradigm of AI-generated content (AIGC), there has been increasing attention to transferring knowledge from pre-trained text-to-image (T2I) models to text-to-video (T2V) gen…
Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation
Junyoung Seo, Wooseok Jang, Min-Seop Kwak +6
Text-to-3D generation has shown rapid progress in recent days with the advent of score distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neura…
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic Models
Gyeongnyeon Kim, Wooseok Jang, Gyuseong Lee +3
Generative models have recently undergone significant advancement due to the diffusion models. The success of these models can be often attributed to their use of guidance techniqu…
DiffFace: Diffusion-based Face Swapping with Facial Guidance
Kihong Kim, Yunho Kim, Seokju Cho +5
In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, an…
ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization
Jiwon Kim, Youngjo Min, Daehwan Kim +4
We present a novel semi-supervised learning framework that intelligently leverages the consistency regularization between the model's predictions from two strongly-augmented views…
MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image Translation
Junyoung Seo, Gyuseong Lee, Seokju Cho +2
We present a novel method for exemplar-based image translation, called matching interleaved diffusion models (MIDMs). Most existing methods for this task were formulated as GAN-bas…
Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels
Jiwon Kim, Kwangrok Ryoo, Junyoung Seo +4
Establishing dense correspondences across semantically similar images remains a challenging task due to the significant intra-class variations and background clutters. Traditionall…
AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning
Jiwon Kim, Kwangrok Ryoo, Gyuseong Lee +5
Semi-supervised learning (SSL) has recently proven to be an effective paradigm for leveraging a huge amount of unlabeled data while mitigating the reliance on large labeled data. C…