no code implementations • 31 Jan 2024 • Xiaoyu Li, Qi Zhang, Di Kang, Weihao Cheng, Yiming Gao, Jingbo Zhang, Zhihao Liang, Jing Liao, Yan-Pei Cao, Ying Shan
In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications.
no code implementations • 28 Nov 2023 • Jingbo Zhang, Xiaoyu Li, Qi Zhang, YanPei Cao, Ying Shan, Jing Liao
Optimization-based methods that lift text-to-image diffusion models to 3D generation often fail to preserve the texture details of the reference image, resulting in inconsistent appearances in different views.
no code implementations • 18 Oct 2023 • Hongliang Zhong, Jingbo Zhang, Jing Liao
By discretizing the materials, our model can reduce noise in the decomposition process and generate a segmentation map of discrete materials.
1 code implementation • 19 May 2023 • Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Can Wang, Jing Liao
Extensive experiments demonstrate that our Text2NeRF outperforms existing methods in producing photo-realistic, multi-view consistent, and diverse 3D scenes from a variety of natural language prompts.
1 code implementation • ICCV 2023 • Ruixiang Jiang, Can Wang, Jingbo Zhang, Menglei Chai, Mingming He, Dongdong Chen, Jing Liao
Neural implicit fields are powerful for representing 3D scenes and generating high-quality novel views, but it remains challenging to use such implicit representations for creating a 3D human avatar with a specific identity and artistic style that can be easily animated.
no code implementations • 15 Aug 2022 • Jingbo Zhang, Ziyu Wan, Jing Liao
Due to inevitable noises introduced during scanning and quantization, 3D reconstruction via RGB-D sensors suffers from errors both in geometry and texture, leading to artifacts such as camera drifting, mesh distortion, texture ghosting, and blurriness.
1 code implementation • 11 Aug 2022 • Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Can Wang, Jing Liao
Unlike existing dynamic NeRFs that require dense images as input and can only be modeled for a single identity, our method enables face reconstruction across different persons with few-shot inputs.
4 code implementations • ICCV 2021 • Ziyu Wan, Jingbo Zhang, Dongdong Chen, Jing Liao
Image completion has made tremendous progress with convolutional neural networks (CNNs), because of their powerful texture modeling capacity.
Ranked #6 on Image Inpainting on CelebA-HQ
no code implementations • 22 Nov 2017 • Tong Mo, Rong Zhang, Weiping Li, Jingbo Zhang, Zhonghai Wu, Wei Tan
The practice in an elderly-care company shows that the FPQM can reduce the number of attributes by 90. 56% with a prediction accuracy of 98. 39%.