no code implementations • 18 Mar 2024 • Jiaxiang Tang, Ruijie Lu, Xiaokang Chen, Xiang Wen, Gang Zeng, Ziwei Liu
Text-to-texture synthesis has become a new frontier in 3D content creation thanks to the recent advances in text-to-image models.
1 code implementation • 4 Mar 2024 • Fangzhou Hong, Jiaxiang Tang, Ziang Cao, Min Shi, Tong Wu, Zhaoxi Chen, Shuai Yang, Tengfei Wang, Liang Pan, Dahua Lin, Ziwei Liu
Specifically, it is powered by a text-conditioned tri-plane latent diffusion model, which quickly generates coarse 3D samples for fast prototyping.
1 code implementation • 7 Feb 2024 • Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, Ziwei Liu
2) 3D Backbone: We present an asymmetric U-Net as a high-throughput backbone operating on multi-view images, which can be produced from text or single-view image input by leveraging multi-view diffusion models.
1 code implementation • 28 Dec 2023 • Jiawei Ren, Liang Pan, Jiaxiang Tang, Chi Zhang, Ang Cao, Gang Zeng, Ziwei Liu
Remarkable progress has been made in 4D content generation recently.
no code implementations • 28 Nov 2023 • Xian Liu, Xiaohang Zhan, Jiaxiang Tang, Ying Shan, Gang Zeng, Dahua Lin, Xihui Liu, Ziwei Liu
In this paper, we propose an efficient yet effective framework, HumanGaussian, that generates high-quality 3D humans with fine-grained geometry and realistic appearance.
1 code implementation • 28 Sep 2023 • Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, Gang Zeng
In contrast to the occupancy pruning used in Neural Radiance Fields, we demonstrate that the progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks.
1 code implementation • 16 Aug 2023 • Yangyi Huang, Hongwei Yi, Yuliang Xiu, Tingting Liao, Jiaxiang Tang, Deng Cai, Justus Thies
But how to effectively capture all visual attributes of an individual from a single image, which are sufficient to reconstruct unseen areas (e. g., the back view)?
no code implementations • 25 May 2023 • Xiaokang Chen, Jiaxiang Tang, Diwen Wan, Jingbo Wang, Gang Zeng
We propose to imitate the backbone feature of off-the-shelf perception models to achieve zero-shot semantic segmentation with NeRF.
1 code implementation • ICCV 2023 • Jiaxiang Tang, Hang Zhou, Xiaokang Chen, Tianshu Hu, Errui Ding, Jingdong Wang, Gang Zeng
Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction.
1 code implementation • 22 Nov 2022 • Jiaxiang Tang, Kaisiyuan Wang, Hang Zhou, Xiaokang Chen, Dongliang He, Tianshu Hu, Jingtuo Liu, Gang Zeng, Jingdong Wang
While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their potential usage.
no code implementations • 18 Jun 2022 • Jiaxiang Tang, Jinbao Zhu, Songze Li, Lichao Sun
We consider a federated representation learning framework, where with the assistance of a central server, a group of $N$ distributed clients train collaboratively over their private data, for the representations (or embeddings) of a set of entities (e. g., users in a social network).
2 code implementations • 30 May 2022 • Jiaxiang Tang, Xiaokang Chen, Jingbo Wang, Gang Zeng
To circumvent the hurdle, in this paper, we present an explicit neural field representation that enables efficient and convenient manipulation of models.
1 code implementation • 31 Mar 2022 • Jiaxiang Tang, Xiaokang Chen, Jingbo Wang, Gang Zeng
Semantic scene reconstruction from point cloud is an essential and challenging task for 3D scene understanding.
no code implementations • 24 Dec 2021 • Xiaokang Chen, Jiaxiang Tang, Jingbo Wang, Gang Zeng
Firstly, we transfer the voxelized scenes to point clouds by removing these visible empty voxels and adopt a deep point stream to capture semantic information from the scene efficiently.
Ranked #4 on 3D Semantic Scene Completion on NYUv2
1 code implementation • 19 Jul 2021 • Jiaxiang Tang, Xiaokang Chen, Gang Zeng
Inspired by the recent progress in implicit neural representation, we propose to formulate the guided super-resolution as a neural implicit image interpolation problem, where we take the form of a general image interpolation but use a novel Joint Implicit Image Function (JIIF) representation to learn both the interpolation weights and values.
1 code implementation • 11 Sep 2019 • Jiaxiang Tang, Wei Hu, Xiang Gao, Zongming Guo
In particular, we cast the graph optimization problem as distance metric learning to capture pairwise similarities of features in each layer.
no code implementations • 29 Nov 2018 • Xiang Gao, Wei Hu, Jiaxiang Tang, Jiaying Liu, Zongming Guo
In this paper, we represent skeletons naturally on graphs, and propose a graph regression based GCN (GR-GCN) for skeleton-based action recognition, aiming to capture the spatio-temporal variation in the data.
Ranked #2 on Skeleton Based Action Recognition on Florence 3D