9 papers
GraspHOI: Full-Body 3D Human-Object Reconstruction with Finger-Level Grasps from a Single In-the-Wild Image
Semin Kim, Haechan Shin, Jongyoo Kim
Existing monocular full-body 3D human-object interaction (HOI) methods do not combine explicit finger-level grasp optimization with category-agnostic object reconstruction. Despite…
Root-Selecting Fixed-Point Inversion for Rectified Flows via Trajectory Straightness
Semin Kim, Jihwan Yoon, Seunghoon Hong
Finding the initial noise that generates a given data sample, known as inversion, is a key component for downstream applications such as training-free image editing. Existing fixed…
Training-Free Refinement of Flow Matching with Divergence-based Sampling
Yeonwoo Cha, Jaehoon Yoo, Semin Kim +3
Flow-based models learn a target distribution by modeling a marginal velocity field, defined as the average of sample-wise velocities connecting each sample from a simple prior to…
FlowBind: Efficient Any-to-Any Generation with Bidirectional Flows
Yeonwoo Cha, Semin Kim, Jinhyeon Kwon +1
Any-to-any generation seeks to translate between arbitrary subsets of modalities, enabling flexible cross-modal synthesis. Despite recent success, existing flow-based approaches ar…
Bridging the gap to real-world language-grounded visual concept learning
Whie Jung, Semin Kim, Junee Kim +1
Human intelligence effortlessly interprets visual scenes along a rich spectrum of semantic dimensions. However, existing approaches to language-grounded visual concept learning are…
Reward-Agnostic Prompt Optimization for Text-to-Image Diffusion Models
Semin Kim, Yeonwoo Cha, Jaehoon Yoo +1
We investigate a general approach for improving user prompts in text-to-image (T2I) diffusion models by finding prompts that maximize a reward function specified at test-time. Alth…
RA-Touch: Retrieval-Augmented Touch Understanding with Enriched Visual Data
Yoorhim Cho, Hongyeob Kim, Semin Kim +3
Visuo-tactile perception aims to understand an object's tactile properties, such as texture, softness, and rigidity. However, the field remains underexplored because collecting tac…
Simulation-Free Training of Neural ODEs on Paired Data
Semin Kim, Jaehoon Yoo, Jinwoo Kim +3
In this work, we investigate a method for simulation-free training of Neural Ordinary Differential Equations (NODEs) for learning deterministic mappings between paired data. Despit…
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild
Donggyun Kim, Seongwoong Cho, Semin Kim +2
Large language models have evolved data-efficient generalists, benefiting from the universal language interface and large-scale pre-training. However, constructing a data-efficient…