1 code implementation • 26 Sep 2024 • Huan Wang, Feitong Tan, Ziqian Bai, yinda zhang, Shichen Liu, Qiangeng Xu, Menglei Chai, Anish Prabhu, Rohit Pandey, Sean Fanello, Zeng Huang, Yun Fu
Recent works have shown that neural radiance fields (NeRFs) on top of parametric models have reached SOTA quality to build photorealistic head avatars from a monocular video.
no code implementations • 26 Aug 2024 • Shichen Liu, Huaxing Lu
This paper employs deep learning methods to investigate the visual similarity of ethnic minority patterns in Southwest China.
no code implementations • CVPR 2024 • Ziqian Bai, Feitong Tan, Sean Fanello, Rohit Pandey, Mingsong Dou, Shichen Liu, Ping Tan, yinda zhang
To address these challenges, we propose a novel fast 3D neural implicit head avatar model that achieves real-time rendering while maintaining fine-grained controllability and high rendering quality.
no code implementations • 8 Dec 2023 • Zhen Wang, Qiangeng Xu, Feitong Tan, Menglei Chai, Shichen Liu, Rohit Pandey, Sean Fanello, Achuta Kadambi, yinda zhang
State-of-the-art results from extensive experiments demonstrate MVDD's excellent ability in 3D shape generation, depth completion, and its potential as a 3D prior for downstream tasks.
no code implementations • ICCV 2023 • Haiwen Feng, Peter Kulits, Shichen Liu, Michael J. Black, Victoria Abrevaya
Learning-based methods address this but do not generalize well when the input pose is far from those seen during training.
no code implementations • CVPR 2022 • Haiwei Chen, Jiayi Liu, Weikai Chen, Shichen Liu, Yajie Zhao
In this paper, we propose an exemplar-based visual pattern synthesis framework that aims to model the inner statistics of visual patterns and generate new, versatile patterns that meet the aforementioned requirements.
no code implementations • ICCV 2021 • Tianye Li, Shichen Liu, Timo Bolkart, Jiayi Liu, Hao Li, Yajie Zhao
We propose ToFu, Topologically consistent Face from multi-view, a geometry inference framework that can produce topologically consistent meshes across facial identities and expressions using a volumetric representation instead of an explicit underlying 3DMM.
2 code implementations • CVPR 2021 • Yichao Zhou, Shichen Liu, Yi Ma
Recent advances have shown that symmetry, a structural prior that most objects exhibit, can support a variety of single-view 3D understanding tasks.
1 code implementation • CVPR 2021 • Haiwei Chen, Shichen Liu, Weikai Chen, Hao Li
Features that are equivariant to a larger group of symmetries have been shown to be more discriminative and powerful in recent studies.
no code implementations • ICCV 2021 • Shichen Liu, Yichao Zhou, Yajie Zhao
Being able to infer 3D structures from 2D images with geometric principles, vanishing points have been a well-recognized concept in 3D vision research.
1 code implementation • 7 Aug 2020 • Yichao Zhou, Jingwei Huang, Xili Dai, Shichen Liu, Linjie Luo, Zhili Chen, Yi Ma
We present HoliCity, a city-scale 3D dataset with rich structural information.
2 code implementations • 17 Jun 2020 • Yichao Zhou, Shichen Liu, Yi Ma
In this work, we focus on object-level 3D reconstruction and present a geometry-based end-to-end deep learning framework that first detects the mirror plane of reflection symmetry that commonly exists in man-made objects and then predicts depth maps by finding the intra-image pixel-wise correspondence of the symmetry.
no code implementations • NeurIPS 2019 • Shichen Liu, Shunsuke Saito, Weikai Chen, Hao Li
The representation of 3D surfaces itself is a key factor for the quality and resolution of the 3D output.
2 code implementations • ICCV 2019 • Shichen Liu, Tianye Li, Weikai Chen, Hao Li
Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation.
Ranked #1 on 3D Object Reconstruction on ShapeNet
no code implementations • 17 Jan 2019 • Shichen Liu, Weikai Chen, Tianye Li, Hao Li
We also show that our soft rasterizer can achieve comparable results to the cutting-edge supervised learning method and in various cases even better ones, especially for real-world data.
no code implementations • NeurIPS 2018 • Shichen Liu, Mingsheng Long, Jian-Min Wang, Michael. I. Jordan
A technical challenge of deep learning is recognizing target classes without seen data.
no code implementations • 17 Sep 2018 • Jun Feng, Heng Li, Minlie Huang, Shichen Liu, Wenwu Ou, Zhirong Wang, Xiaoyan Zhu
The first one is lack of collaboration between scenarios meaning that each strategy maximizes its own objective but ignores the goals of other strategies, leading to a sub-optimal overall performance.
Multi-agent Reinforcement Learning reinforcement-learning +2
no code implementations • 28 May 2018 • Yabo Ni, Dan Ou, Shichen Liu, Xiang Li, Wenwu Ou, An-Xiang Zeng, Luo Si
In this work, we propose to learn universal user representations across multiple tasks for more e ective personalization.
6 code implementations • CVPR 2018 • Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger
It combines dense connectivity with a novel module called learned group convolution.
no code implementations • CVPR 2017 • Yue Cao, Mingsheng Long, Jian-Min Wang, Shichen Liu
This paper presents a compact coding solution with a focus on the deep learning to quantization approach, which improves retrieval quality by end-to-end representation learning and compact encoding and has already shown the superior performance over the hashing solutions for similarity retrieval.
no code implementations • 7 Jun 2017 • Shichen Liu, Fei Xiao, Wenwu Ou, Luo Si
Real-world search applications often involve multiple factors of preferences or constraints with respect to user experience and computational costs such as search accuracy, search latency, size of search results and total CPU cost, while most existing search solutions only address one or two factors; 2).