most citedLLM-based Human Simulations Have Not Yet Been Reliable

5 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2025

TeleWorld: Towards Dynamic Multimodal Synthesis with a 4D World Model

Yabo Chen, Yuanzhi Liang, Jiepeng Wang +24

World models aim to endow AI systems with the ability to represent, generate, and interact with dynamic environments in a coherent and temporally consistent manner. While recent vi…

cs.CV2025

AvatarVTON: 4D Virtual Try-On for Animatable Avatars

Zicheng Jiang, Jixin Gao, Shengfeng He +5

We propose AvatarVTON, the first 4D virtual try-on framework that generates realistic try-on results from a single in-shop garment image, enabling free pose control, novel-view ren…

eess.IV2025

Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI with Explicit Cardiac Motion Modeling

Yilin Lyu, Fan Yang, Xiaoyue Liu +9

Accurate representation of myocardial infarct geometry is crucial for patient-specific cardiac modeling in MI patients. While Late gadolinium enhancement (LGE) MRI is the clinical…

cs.CL2025★ 5 cited

LLM-based Human Simulations Have Not Yet Been Reliable

Qian Wang, Jiaying Wu, Zichen Jiang +6

Large Language Models (LLMs) are increasingly employed for simulating human behaviors across diverse domains. However, our position is that current LLM-based human simulations rema…

cs.CV2024

D-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On

Zhaotong Yang, Zicheng Jiang, Xinzhe Li +4

In this paper, we introduce D-VTON, an innovative solution for image-based virtual try-on. We address challenges from previous studies, such as semantic inconsistencies before…