14 citations · 14 across the 8 of their papers we have counts for
8 papers
Delving into Asymmetric Information Dynamics for High-Fidelity Virtual Try-On
Zishu Qin, Zhiyu Jin, Pipei Huang +1
Virtual try-on (VTON) requires precise pixel-level fidelity, yet mainstream Diffusion Transformers (DiTs) often suffer from texture degradation and structural drift. We identify sy…
RAGDiffusion++: From Macro-Retrieval to Micro-Fidelity Alignment for Garment Generation
Yuhan Li, Xianfeng Tan, Fangao Zeng +6
Standard clothing asset generation---restoring forward-facing flat-lay garment images from diverse real-world contexts---holds immense commercial value yet demands both macroscopic…
RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation
Yuhan Li, Fangao Zeng, Sicong Kang +7
Efficient text-to-image generation requires both reinforcement-learning (RL)-based reward alignment and few-step distillation, yet these procedures are typically performed sequenti…
TRUST: Efficient Abdominal Trauma Recognition via Image-to-Ultrasound-Video Transfer Learning
Enguang Wang, Hao Zhou, Shuo Gao +2
Abdominal ultrasound is indispensable for rapid, noninvasive trauma triage. However, interpreting the subtle dynamic cues embedded in continuous scanning is time-intensive and oper…
Image Diffusion Preview with Consistency Solver
Fu-Yun Wang, Hao Zhou, Liangzhe Yuan +8
The slow inference process of image diffusion models significantly degrades interactive user experiences. To address this, we introduce Diffusion Preview, a novel paradigm employin…
Video Creation by Demonstration
Yihong Sun, Hao Zhou, Liangzhe Yuan +7
We explore a novel video creation experience, namely Video Creation by Demonstration. Given a demonstration video and a context image from a different scene, we generate a physical…
Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models
Xiaoyu Zhu, Hao Zhou, Pengfei Xing +6
In this paper, we investigate the use of diffusion models which are pre-trained on large-scale image-caption pairs for open-vocabulary 3D semantic understanding. We propose a novel…
VideoPrism: A Foundational Visual Encoder for Video Understanding
Long Zhao, Nitesh B. Gundavarapu, Liangzhe Yuan +16
We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model. We pretrain VideoPrism on a heterogeneous corpus…