no code implementations • 25 Sep 2024 • Zhiyuan Gao, Wenbin Teng, Gonglin Chen, Jinsen Wu, Ningli Xu, Rongjun Qin, Andrew Feng, Yajie Zhao
In this paper, we introduce Skyeyes, a novel framework that can generate photorealistic sequences of ground view images using only aerial view inputs, thereby creating a ground roaming experience.
no code implementations • 3 Sep 2024 • Gonglin Chen, Jinsen Wu, Haiwei Chen, Wenbin Teng, Zhiyuan Gao, Andrew Feng, Rongjun Qin, Yajie Zhao
Our method formulates geometric verification as an optimization problem, guiding feature matching within detector-free methods and using sparse correspondences from detector-based methods as anchor points.
no code implementations • 31 Jul 2024 • Hanyuan Xiao, Yingshu Chen, Huajian Huang, Haolin Xiong, Jing Yang, Pratusha Prasad, Yajie Zhao
In the second Texture Enhancement step, we introduce a novel Depth-guided Inpainting Score Distillation Sampling (DI-SDS), which enhances geometry and texture details with the inpainting diffusion prior, beyond the scope of the 3D-aware diffusion prior knowledge in the first coarse step.
no code implementations • CVPR 2024 • Haiwei Chen, Yajie Zhao
We present a method for large-mask pluralistic image inpainting based on the generative framework of discrete latent codes.
no code implementations • 15 Mar 2024 • Liupei Lu, Yufeng Yin, Yuming Gu, Yizhen Wu, Pratusha Prasad, Yajie Zhao, Mohammad Soleymani
Then, we use MSDA to transfer the AU detection knowledge from a real dataset and the synthetic dataset to a target dataset.
1 code implementation • ICLR 2023 • Jing Yang, Hanyuan Xiao, Wenbin Teng, Yunxuan Cai, Yajie Zhao
Extensive experiments showcase the quality and efficiency of our PBR face skin shader, indicating the effectiveness of our proposed lighting and material representations.
no code implementations • 21 Feb 2023 • Pengda Xiang, Sitao Xiang, Yajie Zhao
Can we customize a deep generative model which can generate images that can match the texture of some given image?
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 • 13 Dec 2021 • Zhengfei Kuang, Jiaman Li, Mingming He, Tong Wang, Yajie Zhao
To make the local features aware of the global context and improve their matching accuracy, we introduce DenseGAP, a new solution for efficient Dense correspondence learning with a Graph-structured neural network conditioned on Anchor Points.
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.
1 code implementation • ICCV 2021 • Sitao Xiang, Yuming Gu, Pengda Xiang, Menglei Chai, Hao Li, Yajie Zhao, Mingming He
In this paper, we adopt a general setting where all factors that are hard to label or identify are encapsulated as a single unknown factor.
no code implementations • 7 Jun 2021 • Jiaman Li, Ruben Villegas, Duygu Ceylan, Jimei Yang, Zhengfei Kuang, Hao Li, Yajie Zhao
We demonstrate the effectiveness of our hierarchical motion variational autoencoder in a variety of tasks including video-based human pose estimation, motion completion from partial observations, and motion synthesis from sparse key-frames.
Ranked #4 on Motion Synthesis on LaFAN1
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.
no code implementations • 1 Oct 2020 • Jiaman Li, Zheng-Fei Kuang, Yajie Zhao, Mingming He, Karl Bladin, Hao Li
We also model the joint distribution between identities and expressions, enabling the inference of the full set of personalized blendshapes with dynamic appearances from a single neutral input scan.
1 code implementation • CVPR 2020 • Ruilong Li, Karl Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Prasad, Bipin Kishore, Jun Xing, Hao Li
Based on a combined data set of 4000 high resolution facial scans, we introduce a non-linear morphable face model, capable of producing multifarious face geometry of pore-level resolution, coupled with material attributes for use in physically-based rendering.
no code implementations • ICCV 2019 • Yajie Zhao, Zeng Huang, Tianye Li, Weikai Chen, Chloe LeGendre, Xinglei Ren, Jun Xing, Ari Shapiro, Hao Li
In contrast to the previous state-of-the-art approach, our method handles even portraits with extreme perspective distortion, as we avoid the inaccurate and error-prone step of first fitting a 3D face model.
no code implementations • 11 Dec 2018 • Weikai Chen, Xiaoguang Han, Guanbin Li, Chao Chen, Jun Xing, Yajie Zhao, Hao Li
Three-dimensional object recognition has recently achieved great progress thanks to the development of effective point cloud-based learning frameworks, such as PointNet and its extensions.
no code implementations • ECCV 2018 • Zeng Huang, Tianye Li, Weikai Chen, Yajie Zhao, Jun Xing, Chloe LeGendre, Linjie Luo, Chongyang Ma, Hao Li
We present a deep learning-based volumetric capture approach for performance capture using a passive and highly sparse multi-view capture system.
no code implementations • 23 Jul 2018 • Yajie Zhao, Weikai Chen, Jun Xing, Xiaoming Li, Zach Bessinger, Fuchang Liu, WangMeng Zuo, Ruigang Yang
Different from the state-of-the-art face inpainting methods that have no control over the synthesized content and can only handle frontal face pose, our approach can faithfully recover the missing content under various head poses while preserving the identity.
no code implementations • 26 Oct 2016 • Yajie Zhao, Qingguo Xu, Xinyu Huang, Ruigang Yang
The main purpose of this paper is to synthesize realistic face images without occlusions based on the images captured by these cameras.