activity
20222026
most citedDelving into Multimodal Prompting for Fine-grained Visual Classification

3 citations · 14 across the 15 of their papers we have counts for

collaborators

19 papers

cs.CV2026

Demiurge: Forging Navigable 4D Worlds via Autoregressive Video Rerendering

Hanxin Zhu, Jianqi Ma, Cong Wang +10

Recent advances in video diffusion enable novel-view video synthesis from a single input video, supporting world modeling, embodied intelligence, and robotic simulation. However, e…

cs.CV2026

MegaAvatar: Controllable Talking Avatar Generation

Junyao Gao, Sibo Liu, Weidong Zhang +2

This report presents \textbf{MegaAvatar}, a controllable talking avatar generation framework built on top of the Wan2.2-TI2V-5B model. Compared with previous talking-avatar methods…

cs.CV2026

MegaStyle++: Scaling Image Style Space through Hierarchical Style Definition

Junyao Gao, Sibo Liu, Jiaxing Li +4

Image style is a highly abstract, human-constructed concept shaped by a range of visual factors and intrinsically entangled with content, yet a unified and explicit definition of i…

cs.CV2026

DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models

Xin Jiang, Hao Tang, Junyao Gao +7

Recent advances in generative models have shifted AI-generated image detection from identifying easily distinguishable, fully synthetic images to identifying highly realistic conte…

cs.CV2026

DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation

Jiaxing Li, Kai Zou, Cindy Zhou +7

Existing autoregressive video distillation methods commonly adopt a Distribution Matching Distillation (DMD)-based multi-stage pipeline. However, they typically decouple the initia…

cs.CV2026★ 1 cited

MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping

Junyao Gao, Sibo Liu, Jiaxing Li +6

In this paper, we introduce MegaStyle, a novel and scalable data curation pipeline that constructs an intra-style consistent, inter-style diverse and high-quality style dataset. We…

cs.MA2025

Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading

Zifan Song, Kaitao Song, Guosheng Hu +5

Recent advancements in large language models (LLMs) and agentic systems have shown exceptional decision-making capabilities, revealing significant potential for autonomic finance.…

cs.CV2025

CamPVG: Camera-Controlled Panoramic Video Generation with Epipolar-Aware Diffusion

Chenhao Ji, Chaohui Yu, Junyao Gao +2

Recently, camera-controlled video generation has seen rapid development, offering more precise control over video generation. However, existing methods predominantly focus on camer…

cs.CV2025★ 1 cited

CharacterShot: Controllable and Consistent 4D Character Animation

Junyao Gao, Jiaxing Li, Wenran Liu +5

In this paper, we propose \textbf{CharacterShot}, a controllable and consistent 4D character animation framework that enables any individual designer to create dynamic 3D character…

cs.CV2025

One Object, Multiple Lies: A Benchmark for Cross-task Adversarial Attack on Unified Vision-Language Models

Jiale Zhao, Xinyang Jiang, Junyao Gao +2

Unified vision-language models(VLMs) have recently shown remarkable progress, enabling a single model to flexibly address diverse tasks through different instructions within a shar…

cs.CV2025

TRAIL: Transferable Robust Adversarial Images via Latent diffusion

Yuhao Xue, Zhifei Zhang, Xinyang Jiang +6

Adversarial attacks exploiting unrestricted natural perturbations present severe security risks to deep learning systems, yet their transferability across models remains limited du…

cs.CV2025★ 1 cited

FaceShot: Bring Any Character into Life

Junyao Gao, Yanan Sun, Fei Shen +4

In this paper, we present FaceShot, a novel training-free portrait animation framework designed to bring any character into life from any driven video without fine-tuning or retrai…

cs.AI2025

LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?

Kexian Tang, Junyao Gao, Yanhong Zeng +6

Many real-world applications of spatial intelligence, such as robotic control, autonomous driving, and automated assembly, require spatial reasoning across multiple sequential step…

cs.CV2025★ 3 cited

Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion Model

Fei Shen, Cong Wang, Junyao Gao +4

Recent advances in conditional diffusion models have shown promise for generating realistic TalkingFace videos, yet challenges persist in achieving consistent head movement, synchr…

cs.CV2024★ 2 cited

DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion

Weicai Ye, Chenhao Ji, Zheng Chen +7

Diffusion-based methods have achieved remarkable achievements in 2D image or 3D object generation, however, the generation of 3D scenes and even images remains constr…

cs.CV2024★ 2 cited

StyleShot: A Snapshot on Any Style

Junyao Gao, Yanchen Liu, Yanan Sun +4

In this paper, we show that, a good style representation is crucial and sufficient for generalized style transfer without test-time tuning. We achieve this through constructing a s…

cs.CV2024★ 1 cited

DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification

Shuguang Dou, Xiangyang Jiang, Yuanpeng Tu +4

The paper introduces the Decouple Re-identificatiOn and human Parsing (DROP) method for occluded person re-identification (ReID). Unlike mainstream approaches using global features…

cs.CV2023★ 3 cited

Delving into Multimodal Prompting for Fine-grained Visual Classification

Xin Jiang, Hao Tang, Junyao Gao +3

Fine-grained visual classification (FGVC) involves categorizing fine subdivisions within a broader category, which poses challenges due to subtle inter-class discrepancies and larg…

cs.CR2022

Similarity Distribution based Membership Inference Attack on Person Re-identification

Junyao Gao, Xinyang Jiang, Huishuai Zhang +6

While person Re-identification (Re-ID) has progressed rapidly due to its wide real-world applications, it also causes severe risks of leaking personal information from training dat…