36 citations · 51 across the 16 of their papers we have counts for
16 papers
WorldKV: Efficient World Memory with World Retrieval and Compression
Jung Yi, Minjae Kim, Paul Hyunbin Cho +3
Autoregressive video diffusion models have enabled real-time, action-conditioned world generation. However, sustaining a persistent world, where revisiting a previously seen viewpo…
Repurposing Geometric Foundation Models for Multi-view Diffusion
Wooseok Jang, Seonghu Jeon, Jisang Han +5
While recent advances in generative latent spaces have driven substantial progress in single-image generation, the optimal latent space for novel view synthesis (NVS) remains large…
APPLE: Attribute-Preserving Pseudo-Labeling for Diffusion-Based Face Swapping
Jiwon Kang, Yeji Choi, JoungBin Lee +6
Face swapping aims to transfer the identity of a source face onto a target face while preserving target-specific attributes such as pose, expression, lighting, skin tone, and makeu…
V-Warper: Appearance-Consistent Video Diffusion Personalization via Value Warping
Hyunkoo Lee, Wooseok Jang, Jini Yang +4
Video personalization aims to generate videos that faithfully reflect a user-provided subject while following a text prompt. However, existing approaches often rely on heavy video-…
Deep Forcing: Training-Free Long Video Generation with Deep Sink and Participative Compression
Jung Yi, Wooseok Jang, Paul Hyunbin Cho +3
Recent advances in autoregressive video diffusion have enabled real-time frame streaming, yet existing solutions still suffer from temporal repetition, drift, and motion decelerati…
Emergent Outlier View Rejection in Visual Geometry Grounded Transformers
Jisang Han, Sunghwan Hong, Jaewoo Jung +5
Reliable 3D reconstruction from in-the-wild image collections is often hindered by "noisy" images-irrelevant inputs with little or no view overlap with others. While traditional St…
Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models
Donghoon Ahn, Jiwon Kang, Sanghyun Lee +7
Recent guidance methods in diffusion models steer reverse sampling by perturbing the model to construct an implicit weak model and guide generation away from it. Among these approa…
A Noise is Worth Diffusion Guidance
Donghoon Ahn, Jiwon Kang, Sanghyun Lee +9
Diffusion models excel in generating high-quality images. However, current diffusion models struggle to produce reliable images without guidance methods, such as classifier-free gu…
ControlFace: Harnessing Facial Parametric Control for Face Rigging
Wooseok Jang, Youngjun Hong, Geonho Cha +1
Manipulation of facial images to meet specific controls such as pose, expression, and lighting, also known as face rigging, is a complex task in computer vision. Existing methods a…
Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
Donghoon Ahn, Hyoungwon Cho, Jaewon Min +6
Recent studies have demonstrated that diffusion models are capable of generating high-quality samples, but their quality heavily depends on sampling guidance techniques, such as cl…
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…
Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters
Gyuseong Lee, Wooseok Jang, Jinhyeon Kim +2
Learning robust vision models that perform well in out-of-distribution (OOD) situations is an important task for model deployment in real-world settings. Despite extensive research…
User-friendly Image Editing with Minimal Text Input: Leveraging Captioning and Injection Techniques
Sunwoo Kim, Wooseok Jang, Hyunsu Kim +4
Recent text-driven image editing in diffusion models has shown remarkable success. However, the existing methods assume that the user's description sufficiently grounds the context…
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…
Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
Susung Hong, Gyuseong Lee, Wooseok Jang +1
Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity. This success is largely attributed to the use of class- or text-c…