arXiv subjects

Taewook Ha

Publications and source records attributed to Taewook Ha.

9 recordsLinked to original sources

arXiv2026

$\Upsilon(nS)$ Production within Jets at the LHC

Heavy quarkonium production inside jets offers a sensitive probe of QCD dynamics and bound-state formation mechanisms. While recent studies demonstrate that charmonium-in-jet observables effectively discriminate among competing nonrelativistic QCD (NRQCD) long-distance matrix element (LDME) sets, whether this discriminating power persists in the bottomonium sector remains an open question. Here, we present the first phenomenological study of $\Upsilon(1S)$, $\Upsilon(2S)$, and $\Upsilon(3S)$ production inside jets using the fragmenting jet function (FJF) framework at next-to-leading order (NLO), incorporating DGLAP evolution, threshold resummation, and feeddown contributions from higher bottomonium states. In sharp contrast to charmonium, we find that bottomonium-in-jet momentum-fraction ($z_H$) distributions exhibit a universal shape that is remarkably insensitive to the choice of LDME sets. We show that this universality stems from the strong dominance of the S-wave spin-triplet color-octet ($^3S_1^{[8]}$) production mechanism reinforced by $\chi_b$ feeddown transitions. Our predictions capture both the characteristic large-$z_H$ peak and the spectral broadening with increasing jet transverse momentum observed in recent CMS measurements. These results establish a clear physical distinction between charmonium and bottomonium fragmentation inside jets, providing a theoretical benchmark for future high-precision measurements at the LHC.

hep-ph↗

arXiv2026

DeWorldSG: Depth-Aware 3D Semantic Scene Graph Generation via World-Model Priors

We present DeWorldSG, a novel framework that generates spatio-temporally robust 3D Semantic Scene Graphs from RGB-D sequences. Existing methods often struggle to construct reliable 3D scene graphs due to unstable 3D object representations and missing relations caused by frame-wise inference. DeWorldSG addresses these issues by estimating instance-level geometric 3D Gaussian distributions through depth-guided filtering and representing each object as a probabilistic 3D node rather than a single projected point. To mitigate relational sparsity from frame-wise inference, our framework further aggregates spatiotemporal evidence across object pairs and refines relations using contextual priors derived from a world model (V-JEPA 2). Experiments on the 3DSSG and ReplicaSSG datasets demonstrate state-of-the-art (SoTA) performance in both object and predicate prediction, while producing temporally consistent scene structures. In particular, our method improves triplet recall by 77.4% and predicate recall by 23.2% over prior SoTA approaches, making it suitable for robotic manipulation and AR applications. Our code and models are open-sourced.

cs.CV↗

arXiv2026

Int3DNet: Scene-Motion Cross Attention Network for 3D Intention Prediction in Mixed Reality

We propose Int3DNet, a scene-aware network that predicts 3D intention areas directly from scene geometry and head-hand motion cues, enabling robust human intention prediction without explicit object-level perception. In Mixed Reality (MR), intention prediction is critical as it enables the system to anticipate user actions and respond proactively, reducing interaction delays and ensuring seamless user experiences. Our method employs a cross attention fusion of sparse motion cues and scene point clouds, offering a novel approach that directly interprets the user's spatial intention within the scene. We evaluated Int3DNet on MoGaze and CIRCLE datasets, which are public datasets for full-body human-scene interactions, showing consistent performance across time horizons of up to 1500 ms and outperforming the baselines, even in diverse and unseen scenes. Moreover, we demonstrate the usability of proposed method through a demonstration of efficient visual question answering (VQA) based on intention areas. Int3DNet provides reliable 3D intention areas derived from head-hand motion and scene geometry, thus enabling seamless interaction between humans and MR systems through proactive processing of intention areas.

cs.CV↗

arXiv2026

Task Breakpoint Generation using Origin-Centric Graph in Virtual Reality Recordings for Adaptive Playback

We propose a method for generating task breakpoints based on an Origin-Centric Graph (OCG) to segment goal-oriented activity recordings into task units for adaptive playback in Virtual Reality (VR) environments. With the development of Augmented Reality (AR)/VR head-mounted displays (HMDs), research on adaptive tutorials and authoring tools has become active, but existing task segmentation methods mainly rely on manual annotation or are restricted to 2D video which limits their applicability to 3D VR contexts. In our approach, assembly scenarios with clearly defined task boundaries are recorded using a structured spatio-temporal scene graph (STSG), and the OCG is employed to track changes in the central object and the formation of new groups, thereby generating task breakpoints automatically. A user study collected user-perceived task breakpoints to establish ground truth (GT), and comparison with the algorithm-detected breakpoints demonstrated high agreement and confirmed accuracy in supporting adaptive playback. The proposed task segmentation method provides a foundation for dynamically adjusting VR playback according to user proficiency and progress, with potential for extension into automatic timeline segmentation systems for diverse VR recordings.

cs.HC↗

arXiv2026

$J/\psi$ Production Within Jets at the EIC

We present theoretical predictions for the transverse-momentum distribution of $J/\psi$ produced within jets at the upcoming Electron-Ion Collider (EIC). Utilizing the semi-inclusive fragmenting jet function (FJF) framework, our calculation achieves next-to-leading order (NLO) accuracy in the strong coupling and leading-logarithmic (LL) accuracy by resumming both collinear and threshold logarithms. In contrast to the gluon-dominated regime of the LHC, EIC photoproduction is characterized by an enhanced quark-initiated component, offering a complementary probe into the charmonium production mechanism governed by nonperturbative long-distance matrix elements (LDMEs). We examine the impact of representative LDME sets, demonstrating the EIC's distinct discriminating power for the mechanisms. We find that quark contributions are particularly significant in the small momentum fraction region. This region is also shown to be sensitive to both the jet radius $R$ and the experimental muon identification criteria for the $J/\psi \to \mu^+\mu^-$ decay channel. These findings establish quarkonium-in-jet observables at the EIC as a vital, independent probe for constraining the production mechanisms and advancing our understanding of heavy quarkonium formation.

hep-ph↗

arXiv2024

Dense Hand-Object(HO) GraspNet with Full Grasping Taxonomy and Dynamics

Existing datasets for 3D hand-object interaction are limited either in the data cardinality, data variations in interaction scenarios, or the quality of annotations. In this work, we present a comprehensive new training dataset for hand-object interaction called HOGraspNet. It is the only real dataset that captures full grasp taxonomies, providing grasp annotation and wide intraclass variations. Using grasp taxonomies as atomic actions, their space and time combinatorial can represent complex hand activities around objects. We select 22 rigid objects from the YCB dataset and 8 other compound objects using shape and size taxonomies, ensuring coverage of all hand grasp configurations. The dataset includes diverse hand shapes from 99 participants aged 10 to 74, continuous video frames, and a 1.5M RGB-Depth of sparse frames with annotations. It offers labels for 3D hand and object meshes, 3D keypoints, contact maps, and \emph{grasp labels}. Accurate hand and object 3D meshes are obtained by fitting the hand parametric model (MANO) and the hand implicit function (HALO) to multi-view RGBD frames, with the MoCap system only for objects. Note that HALO fitting does not require any parameter tuning, enabling scalability to the dataset's size with comparable accuracy to MANO. We evaluate HOGraspNet on relevant tasks: grasp classification and 3D hand pose estimation. The result shows performance variations based on grasp type and object class, indicating the potential importance of the interaction space captured by our dataset. The provided data aims at learning universal shape priors or foundation models for 3D hand-object interaction. Our dataset and code are available at https://hograspnet2024.github.io/.

cs.CV↗

arXiv2024

Meta-Objects: Interactive and Multisensory Virtual Objects Learned from the Real World for Use in Augmented Reality

We introduce the concept of a meta-object, a next-generation virtual object that inherits the form, properties, and functions of its real-world counterpart, enabling seamless synchronization, interaction, and sharing between the physical and virtual worlds. While plenty of today's virtual objects provide some sensory feedback and dynamic behavior, meta-objects fully integrate interactive and multisensory features within a structured data framework to enable real-time immersive experiences in a post-metaverse intelligent simulation platform. Three key components underpin the utilization of meta-objects in the post-metaverse: property-embedded modeling for physical and action realism, adaptive multisensory feedback tailored to user interactions, and a scene graph-based intelligence simulation platform for scalable and efficient ecosystem integration. By leveraging meta-objects through wearable AR/VR devices, the post-metaverse facilitates seamless interactions that transcend spatial and temporal barriers, paving the way for a transformative reality-virtuality convergence.

cs.HC↗

arXiv2021

N-jettiness in electroweak high-energy processes

We study $N$-jettiness in electroweak processes at extreme high energies. The description of the scattering process such as $e^- e^+ \rightarrow \mu^- \mu^+ +X$ is similar to QCD. At present, electroweak processes are prevailed by the processes induced by the strong interaction, but they will be relevant at future $e^- e^+$ colliders at high energy. The main difference between QCD and electroweak processes is that the initial- and final-state particles should appear in the form of hadrons, that is, color singlets in QCD, while there can be weak nonsinglets as well in electroweak interactions. We analyze the factorization theorems for the $N$-jettiness in $e^- e^+ \rightarrow \mu^- \mu^+ +X$, and compute the factorized parts to next-to-leading logarithmic accuracy. To simplify the comparison with QCD, we only consider the $SU(2)_W$ gauge interaction, and the extension to the Standard Model is straightforward. Put it in a different way, it corresponds to an imaginary world in which colored particles can be observed in QCD, and the richer structure of effective theories is probed. Various nonzero nonsinglet matrix elements are interwoven to produce the factorized results, in contrast to QCD in which there are only contributions from the singlets. Another distinct feature is that the rapidity divergence is prevalent in the contributions from weak nonsinglets due to the different group theory factors between the real and virtual corrections. We verify that the rapidity divergence cancels in all the contributions with a different number of nonsinglet channels. We also consider the renormalization group evolution of each factorized part to resum large logarithms, which are distinct from QCD.

hep-ph↗

arXiv2016

Factorization of the dijet cross section in hadron-hadron collisions

The factorization theorem for the dijet cross section is presented in hadron-hadron collisions with a cone-type jet algorithm. We also apply the beam veto to the beam jets consisting of the initial radiation. The soft-collinear effective theory is employed to see the factorization structure transparently when there are four distinct lightcone directions involved. There are various types of divergences such as the ultraviolet and infrared divergences. And when the phase space is divided to probe the collinear and the soft parts, there appears an additional divergence called rapidity divergence. These divergences are sorted out and we will show that all the infrared and rapidity divergences cancel, and only the ultraviolet divergence remains. It is a vital step to justify the factorization. Among many partonic processes, we take $q\overline{q} \rightarrow gg$ as a specific example to consider the dijet cross section. The hard and the soft functions have nontrivial color structure, while the jet and the beam functions are diagonal in operator basis. The dependence of the soft anomalous dimension on the jet algorithm and the beam veto is diagonal in operator space, and is cancelled by that of the jet and beam functions. We also compute the anomalous dimensions of the factorized components, and resum the large logarithms to next-to-leading logarithmic accuracy by solving the renormalization group equation.

hep-ph↗