9 citations · 15 across the 23 of their papers we have counts for
13 papers · 1 filter
FlowBlending: Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Video Generation
Jibin Song, Mingi Kwon, Jaeseok Jeong +1
In this work, we show that the impact of model capacity varies across timesteps: it is crucial for the early and late stages but largely negligible during the intermediate stage. A…
Geometric Disentanglement of Text Embeddings for Subject-Consistent Text-to-Image Generation using A Single Prompt
Shangxun Li, Youngjung Uh
Text-to-image diffusion models excel at generating high-quality images from natural language descriptions but often fail to preserve subject consistency across multiple outputs, li…
ASemConsist: Adaptive Semantic Feature Control for Training-Free Identity-Consistent Generation
Shin Seong Kim, Minjung Shin, Hyunin Cho +1
Recent text-to-image diffusion models have significantly improved visual quality and text alignment. However, generating a sequence of images while preserving consistent character…
Do Flat Minima Improve Sparse Novel View Synthesis?
Youngsik Yun, Dongjun Gu, Youngjung Uh
Despite the success of recent novel view synthesis methods, they tend to struggle in sparse-view settings. This poor generalization to unseen viewpoints is an inherent challenge wh…
TetraSDF: Analytic Isosurface Extraction with Multi-resolution Tetrahedral Grid
Seonghun Oh, Youngjung Uh, Jin-Hwa Kim
Extracting an explicit surface that exactly matches the zero-level set of a neural signed distance function (SDF) remains challenging. Sampling-based isosurfacing methods such as M…
Balanced conic rectified flow
Shin Seong Kim, Mingi Kwon, Jaeseok Jeong +1
Rectified flow is a generative model that learns smooth transport mappings between two distributions through an ordinary differential equation (ODE). Unlike diffusion-based generat…
StyleKeeper: Prevent Content Leakage using Negative Visual Query Guidance
Jaeseok Jeong, Junho Kim, Gayoung Lee +2
In the domain of text-to-image generation, diffusion models have emerged as powerful tools. Recently, studies on visual prompting, where images are used as prompts, have enabled mo…
MVCustom: Multi-View Customized Diffusion via Geometric Latent Rendering and Completion
Minjung Shin, Hyunin Cho, Sooyeon Go +2
Multi-view generation with camera pose control and prompt-based customization are both essential elements for achieving controllable generative models. However, existing multi-view…
Syncphony: Synchronized Audio-to-Video Generation with Diffusion Transformers
Jibin Song, Mingi Kwon, Jaeseok Jeong +1
Text-to-video and image-to-video generation have made rapid progress in visual quality, but they remain limited in controlling the precise timing of motion. In contrast, audio prov…
TTS-CtrlNet: Time varying emotion aligned text-to-speech generation with ControlNet
Jaeseok Jeong, Yuna Lee, Mingi Kwon +1
Recent advances in text-to-speech (TTS) have enabled natural speech synthesis, but fine-grained, time-varying emotion control remains challenging. Existing methods often allow only…
JAM-Flow: Joint Audio-Motion Synthesis with Flow Matching
Mingi Kwon, Joonghyuk Shin, Jaeseok Jung +2
The intrinsic link between facial motion and speech is often overlooked in generative modeling, where talking head synthesis and text-to-speech (TTS) are typically addressed as sep…
Compensating Spatiotemporally Inconsistent Observations for Online Dynamic 3D Gaussian Splatting
Youngsik Yun, Jeongmin Bae, Hyunseung Son +4
Online reconstruction of dynamic scenes is significant as it enables learning scenes from live-streaming video inputs, while existing offline dynamic reconstruction methods rely on…
TCFG: Tangential Damping Classifier-free Guidance
Mingi Kwon, Shin seong Kim, Jaeseok Jeong. Yi Ting Hsiao +1
Diffusion models have achieved remarkable success in text-to-image synthesis, largely attributed to the use of classifier-free guidance (CFG), which enables high-quality, condition…