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20212023
most citedShuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer

125 citations · 222 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CV2023★ 3 cited

FaceStudio: Put Your Face Everywhere in Seconds

Yuxuan Yan, Chi Zhang, Rui Wang +5

This study investigates identity-preserving image synthesis, an intriguing task in image generation that seeks to maintain a subject's identity while adding a personalized, stylist…

cs.CV2023★ 25 cited

ChartLlama: A Multimodal LLM for Chart Understanding and Generation

Yucheng Han, Chi Zhang, Xin Chen +5

Multi-modal large language models have demonstrated impressive performances on most vision-language tasks. However, the model generally lacks the understanding capabilities for spe…

cs.CV2023

Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering

Chi Zhang, Wei Yin, Gang Yu +5

In this study, we address the challenge of 3D scene structure recovery from monocular depth estimation. While traditional depth estimation methods leverage labeled datasets to dire…

cs.CV2023★ 2 cited

StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data

Yanda Li, Chi Zhang, Gang Yu +6

The remarkable multimodal capabilities demonstrated by OpenAI's GPT-4 have sparked significant interest in the development of multimodal Large Language Models (LLMs). A primary res…

cs.CV2023★ 11 cited

Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation

Zibo Zhao, Wen Liu, Xin Chen +7

We present a novel alignment-before-generation approach to tackle the challenging task of generating general 3D shapes based on 2D images or texts. Directly learning a conditional…

cs.CV2023★ 13 cited

StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation

Chi Zhang, Yiwen Chen, Yijun Fu +7

The recent advancements in image-text diffusion models have stimulated research interest in large-scale 3D generative models. Nevertheless, the limited availability of diverse 3D r…

cs.CV2022★ 14 cited

Executing your Commands via Motion Diffusion in Latent Space

Xin Chen, Biao Jiang, Wen Liu +5

We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or…

cs.CV2022

Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report

Andrey Ignatov, Grigory Malivenko, Radu Timofte +36

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus…

cs.CV2022★ 5 cited

Learning Variational Motion Prior for Video-based Motion Capture

Xin Chen, Zhuo Su, Lingbo Yang +4

Motion capture from a monocular video is fundamental and crucial for us humans to naturally experience and interact with each other in Virtual Reality (VR) and Augmented Reality (A…

cs.CV2022★ 8 cited

Hierarchical Normalization for Robust Monocular Depth Estimation

Chi Zhang, Wei Yin, Zhibin Wang +3

In this paper, we address monocular depth estimation with deep neural networks. To enable training of deep monocular estimation models with various sources of datasets, state-of-th…

cs.CV2021★ 2 cited

Fine-grained Identity Preserving Landmark Synthesis for Face Reenactment

Haichao Zhang, Youcheng Ben, Weixi Zhang +3

Recent face reenactment works are limited by the coarse reference landmarks, leading to unsatisfactory identity preserving performance due to the distribution gap between the manip…

cs.CV2021★ 1 cited

Shuffle Transformer with Feature Alignment for Video Face Parsing

Rui Zhang, Yang Han, Zilong Huang +4

This is a short technical report introducing the solution of the Team TCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR…

cs.CV2021★ 125 cited

Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer

Zilong Huang, Youcheng Ben, Guozhong Luo +3

Very recently, Window-based Transformers, which computed self-attention within non-overlapping local windows, demonstrated promising results on image classification, semantic segme…

eess.IV2021★ 13 cited

Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report

Andrey Ignatov, Grigory Malivenko, David Plowman +35

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually…