1 citations · 1 across the 9 of their papers we have counts for
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WeLike2Party! In-Context Motion Transfer for Multi-Human Image Animation
Sangeyl Lee, Seunghyun Shin, Seungho Park +2
Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation, achieving high-fidelity animat…
Bridging Online and Offline Handwriting via Differentiable Physical Rendering
Seonmi Park, Seunghyun Shin, Vihaan Misra +4
Realistic handwritten text generation plays an important role in numerous applications, such as font design, biometric authentication, and robotic calligraphy. Existing methods are…
JacobianAvatar: Temporally Consistent Semi-rigid Avatar Reconstruction from a Monocular Video
Changyeon Won, Min-Gyu Park, Seonghwan Park +2
Generating realistic human avatars in complex motions--such as clothing dynamics--requires modeling of global and local deformations which remains challenging in monocular settings…
TriMotion: Modality-Agnostic Camera Control for Video Generation
Seunghyun Shin, Jifei Song, Wooseok Jeon +2
Camera motion control is essential for directing viewpoint changes in generative systems. However, existing methods typically condition the generation process on a single specific…
Rebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models
Wooseok Jeon, Seungho Park, Seunghyun Shin +3
Image-to-video models often generate videos that remain overly static, compared to text-to-video models. While prior approaches mitigate this issue by weakening or modifying the im…
ComPose: When to Trust Hands for Object Pose Tracking
Jisu Shin, Junoh Lee, JunGyu Lee +5
Reconstructing the motion of objects from videos is a key component for embodied AI and robot manipulation. While diverse approaches to object pose tracking have been studied, they…
Relaxed Rigidity with Ray-based Grouping for Dynamic Gaussian Splatting
Junoh Lee, Junmyeong Lee, Yeon-Ji Song +4
The reconstruction of dynamic 3D scenes using 3D Gaussian Splatting has shown significant promise. A key challenge, however, remains in modeling realistic motion, as most methods f…
RehearsalNeRF: Decoupling Intrinsic Neural Fields of Dynamic Illuminations for Scene Editing
Changyeon Won, Hyunjun Jung, Jungu Cho +3
Although there has been significant progress in neural radiance fields, an issue on dynamic illumination changes still remains unsolved. Different from relevant works that paramete…
Motion Prior Distillation in Time Reversal Sampling for Generative Inbetweening
Wooseok Jeon, Seunghyun Shin, Dongmin Shin +1
Recent progress in image-to-video (I2V) diffusion models has significantly advanced the field of generative inbetweening, which aims to generate semantically plausible frames betwe…
Universal Image Immunization against Diffusion-based Image Editing via Semantic Injection
Chanhui Lee, Donggyu Choi, Seunghyun Shin +2
Diffusion model advances have enabled powerful text-guided image editing, but also raise ethical and legal risks such as deepfakes and unauthorized use. To prevent these risks, adv…
Inverse Image-Based Rendering for Light Field Generation from Single Images
Hyunjun Jung, Hae-Gon Jeon
A concept of light-fields computed from multiple view images on regular grids has proven its benefit for scene representations, and supported realistic renderings of novel views an…
Video Color Grading via Look-Up Table Generation
Seunghyun Shin, Dongmin Shin, Jisu Shin +2
Different from color correction and transfer, color grading involves adjusting colors for artistic or storytelling purposes in a video, which is used to establish a specific look o…
CHROMA: Consistent Harmonization of Multi-View Appearance via Bilateral Grid Prediction
Jisu Shin, Richard Shaw, Seunghyun Shin +3
Modern camera pipelines apply extensive on-device processing, such as exposure adjustment, white balance, and color correction, which, while beneficial individually, often introduc…
CanonicalFusion: Generating Drivable 3D Human Avatars from Multiple Images
Jisu Shin, Junmyeong Lee, Seongmin Lee +4
We present a novel framework for reconstructing animatable human avatars from multiple images, termed CanonicalFusion. Our central concept involves integrating individual reconstru…
Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation
Ba Hung Ngo, Nhat-Tuong Do-Tran, Tuan-Ngoc Nguyen +2
Most domain adaptation (DA) methods are based on either a convolutional neural networks (CNNs) or a vision transformers (ViTs). They align the distribution differences between doma…