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2 citations · 3 across the 20 of their papers we have counts for

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cs.CV2026

TUE-Detector: A Tool-Using Expert MLLM-Based Detector for AI-Generated Videos

Yichen Wu, Haoxuan Qu, Yongxing Dai +5

AI-generated video detection, which aims to distinguish AI-generated videos from real ones, has recently received increasing research attention. To perform this task reliably, a ke…

cs.CV2026

CANVAS: Consistency-Aware Navigation via Visual Adaptive Sampling for Long-Context Text-to-SVG Generation

Yichen Wu, Haoxuan Qu, Yihang Lou +2

Autoregressive large models have recently advanced Text-to-SVG generation from simple icons to complex, long-context graphics, yet standard autoregressive decoding often fails to m…

cs.CV2026

Training-free Controllable Human Motion Generation under Heterogeneous Constraints

Xiaofei Hui, Bo Yan, Haoxuan Qu +2

Training-free controllable motion generation has attracted growing interest for enabling flexible constraint enforcement without constraint-specific training. However, existing tra…

cs.CV2026

TraMP-LLaMA: Generative Interpretability with Decoupled Instruction Tuning for Facial Expression Quality Assessment

Shuchao Duan, Alan Whone, Hossein Rahmani +2

Existing facial expression quality assessment (FEQA) methods typically produce only a severity score, without explicitly communicating the observable facial motion evidence that su…

cs.CV2026

ToolFG: Towards Well-Grounded Fine-Grained Image Classification

Yu Xue, Haoxuan Qu, Zhuoling Li +4

Fine-grained image classification (FGIC) has broad applications and has attracted significant research attention. In this paper, we explore a novel paradigm for solving FGIC by pro…

cs.CV2026

Translating Signals to Languages for sEMG-Based Activity Recognition

Ming Wang, Haoxuan Qu, Qiuhong Ke +3

Surface electromyography (sEMG) signal-based activity recognition has attracted increasing research attention in recent years. To develop accurate sEMG signal-based activity recogn…

cs.CV2026

MicroscopyMatching: Towards a Ready-to-use Framework for Microscopy Image Analysis in Diverse Conditions

Xiaofei Hui, Haoxuan Qu, Hossein Rahmani +3

Analyzing microscopy images to extract biological object properties (e.g., their morphological organization, temporal dynamics, and population density) is fundamental to various bi…

cs.CV2026

When Visual Privacy Protection Meets Multimodal Large Language Models

Xiaofei Hui, Qian Wu, Haoxuan Qu +3

The emergence of Multimodal Large Language Models (MLLMs) and the widespread usage of MLLM cloud services such as GPT-4V raised great concerns about privacy leakage in visual data.…

cs.CV2025

Learning to Generate Cross-Task Unexploitable Examples

Haoxuan Qu, Qiuchi Xiang, Yujun Cai +4

Unexploitable example generation aims to transform personal images into their unexploitable (unlearnable) versions before they are uploaded online, thereby preventing unauthorized…

cs.CV2025

A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction

Haoxuan Qu, Yujun Cai, Hossein Rahmani +3

Recently, Gaussian Splatting (GS) has received a lot of attention in surface reconstruction. However, while 3D objects can be of complex and diverse shapes in the real world, exist…

cs.CV2025

An Image-like Diffusion Method for Human-Object Interaction Detection

Xiaofei Hui, Haoxuan Qu, Hossein Rahmani +1

Human-object interaction (HOI) detection often faces high levels of ambiguity and indeterminacy, as the same interaction can appear vastly different across different human-object p…

cs.CV2025

CMMLoc: Advancing Text-to-PointCloud Localization with Cauchy-Mixture-Model Based Framework

Yanlong Xu, Haoxuan Qu, Jun Liu +2

The goal of point cloud localization based on linguistic description is to identify a 3D position using textual description in large urban environments, which has potential applica…

cs.CV2025

HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition

Yue Li, Haoxuan Qu, Mengyuan Liu +2

Transformers have demonstrated remarkable performance in skeleton-based human action recognition, yet their quadratic computational complexity remains a bottleneck for real-world a…

cs.CV2024

Off-the-shelf ChatGPT is a Good Few-shot Human Motion Predictor

Haoxuan Qu, Zhaoyang He, Zeyu Hu +2

To facilitate the application of motion prediction in practice, recently, the few-shot motion prediction task has attracted increasing research attention. Yet, in existing few-shot…

cs.CV2024

DisC-GS: Discontinuity-aware Gaussian Splatting

Haoxuan Qu, Zhuoling Li, Hossein Rahmani +2

Recently, Gaussian Splatting, a method that represents a 3D scene as a collection of Gaussian distributions, has gained significant attention in addressing the task of novel view s…

cs.CV2024★ 2 cited

LLMs are Good Action Recognizers

Haoxuan Qu, Yujun Cai, Jun Liu

Skeleton-based action recognition has attracted lots of research attention. Recently, to build an accurate skeleton-based action recognizer, a variety of works have been proposed.…

cs.CV2024

GPT-Connect: Interaction between Text-Driven Human Motion Generator and 3D Scenes in a Training-free Manner

Haoxuan Qu, Ziyan Guo, Jun Liu

Recently, while text-driven human motion generation has received massive research attention, most existing text-driven motion generators are generally only designed to generate mot…

cs.CV2024★ 1 cited

Enhancing Human-Centered Dynamic Scene Understanding via Multiple LLMs Collaborated Reasoning

Hang Zhang, Wenxiao Zhang, Haoxuan Qu +1

Human-centered dynamic scene understanding plays a pivotal role in enhancing the capability of robotic and autonomous systems, in which Video-based Human-Object Interaction (V-HOI)…