5 citations · 11 across the 11 of their papers we have counts for
11 papers
TCAN: Animating Human Images with Temporally Consistent Pose Guidance using Diffusion Models
Jeongho Kim, Min-Jung Kim, Junsoo Lee +1
Pose-driven human-image animation diffusion models have shown remarkable capabilities in realistic human video synthesis. Despite the promising results achieved by previous approac…
SAVE: Protagonist Diversification with Structure Agnostic Video Editing
Yeji Song, Wonsik Shin, Junsoo Lee +2
Driven by the upsurge progress in text-to-image (T2I) generation models, text-to-video (T2V) generation has experienced a significant advance as well. Accordingly, tasks such as mo…
AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks
Kibeom Hong, Seogkyu Jeon, Junsoo Lee +6
To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corre…
LPMM: Intuitive Pose Control for Neural Talking-Head Model via Landmark-Parameter Morphable Model
Kwangho Lee, Patrick Kwon, Myung Ki Lee +2
While current talking head models are capable of generating photorealistic talking head videos, they provide limited pose controllability. Most methods require specific video seque…
Reference-based Image Composition with Sketch via Structure-aware Diffusion Model
Kangyeol Kim, Sunghyun Park, Junsoo Lee +1
Recent remarkable improvements in large-scale text-to-image generative models have shown promising results in generating high-fidelity images. To further enhance editability and en…
Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization
Youngin Cho, Junsoo Lee, Soyoung Yang +6
Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, a…
Continuous-Time Video Generation via Learning Motion Dynamics with Neural ODE
Kangyeol Kim, Sunghyun Park, Junsoo Lee +4
In order to perform unconditional video generation, we must learn the distribution of the real-world videos. In an effort to synthesize high-quality videos, various studies attempt…
AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment
Kangyeol Kim, Sunghyun Park, Jaeseong Lee +3
We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment. Different from previous animation head datasets, we utilize 3D animation mode…
Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation
Sunghyun Park, Kangyeol Kim, Junsoo Lee +4
Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e.g., increas…
Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence
Junsoo Lee, Eungyeup Kim, Yunsung Lee +3
This paper tackles the automatic colorization task of a sketch image given an already-colored reference image. Colorizing a sketch image is in high demand in comics, animation, and…
Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks
Seungjoo Yoo, Hyojin Bahng, Sunghyo Chung +3
Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning. Existing models require a significant amount o…