no code implementations • 25 Jul 2023 • Sicong Tang, Guangyuan Wang, Qing Ran, Lingzhi Li, Li Shen, Ping Tan
We present a novel method for reconstructing clothed humans from a sparse set of, e. g., 1 to 6 RGB images.
no code implementations • 29 May 2023 • Lingzhi Li, Zhongshu Wang, Zhen Shen, Li Shen, Ping Tan
Reconstructing neural radiance fields with explicit volumetric representations, demonstrated by Plenoxels, has shown remarkable advantages on training and rendering efficiency, while grid-based representations typically induce considerable overhead for storage and transmission.
1 code implementation • ICCV 2023 • Xiaoyang Kang, Tao Yang, Wenqi Ouyang, Peiran Ren, Lingzhi Li, Xuansong Xie
Image colorization is a challenging problem due to multi-modal uncertainty and high ill-posedness.
1 code implementation • 9 Dec 2022 • Zhongshu Wang, Lingzhi Li, Zhen Shen, Li Shen, Liefeng Bo
In this paper, we present a novel and effective framework, named 4K-NeRF, to pursue high fidelity view synthesis on the challenging scenarios of ultra high resolutions, building on the methodology of neural radiance fields (NeRF).
1 code implementation • CVPR 2023 • Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Liefeng Bo
Approximating radiance fields with volumetric grids is one of promising directions for improving NeRF, represented by methods like Plenoxels and DVGO, which achieve super-fast training convergence and real-time rendering.
1 code implementation • 26 Oct 2022 • Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Ping Tan
Instead of training a single model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement the adaption of a base model on the current frame.
no code implementations • CVPR 2020 • Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, Fang Wen
In this work, we study various existing benchmarks for deepfake detection researches.
10 code implementations • 31 Dec 2019 • Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, Fang Wen
We propose a novel attributes encoder for extracting multi-level target face attributes, and a new generator with carefully designed Adaptive Attentional Denormalization (AAD) layers to adaptively integrate the identity and the attributes for face synthesis.
4 code implementations • CVPR 2020 • Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, Baining Guo
For this reason, face X-ray provides an effective way for detecting forgery generated by most existing face manipulation algorithms.
no code implementations • COLING 2018 • Wei-Nan Zhang, Yiming Cui, Yifa Wang, Qingfu Zhu, Lingzhi Li, Lianqiang Zhou, Ting Liu
Despite the success of existing works on single-turn conversation generation, taking the coherence in consideration, human conversing is actually a context-sensitive process.