no code implementations • 22 Feb 2024 • Ruifei He, Chuhui Xue, Haoru Tan, Wenqing Zhang, Yingchen Yu, Song Bai, Xiaojuan Qi
Despite its simplicity, we show that IDA shows efficiency and fast convergence in resolving the social bias in TTI diffusion models.
no code implementations • ICCV 2023 • Jiahui Zhang, Fangneng Zhan, Yingchen Yu, Kunhao Liu, Rongliang Wu, Xiaoqin Zhang, Ling Shao, Shijian Lu
However, as the pose estimator is trained with only rendered images, the pose estimation is usually biased or inaccurate for real images due to the domain gap between real images and rendered images, leading to poor robustness for the pose estimation of real images and further local minima in joint optimization.
no code implementations • ICCV 2023 • Muyu Xu, Fangneng Zhan, Jiahui Zhang, Yingchen Yu, Xiaoqin Zhang, Christian Theobalt, Ling Shao, Shijian Lu
Neural Radiance Field (NeRF) has shown impressive performance in novel view synthesis via implicit scene representation.
1 code implementation • NeurIPS 2023 • Kunhao Liu, Fangneng Zhan, Jiahui Zhang, Muyu Xu, Yingchen Yu, Abdulmotaleb El Saddik, Christian Theobalt, Eric Xing, Shijian Lu
Open-vocabulary segmentation of 3D scenes is a fundamental function of human perception and thus a crucial objective in computer vision research.
no code implementations • 18 Apr 2023 • Rongliang Wu, Yingchen Yu, Fangneng Zhan, Jiahui Zhang, Xiaoqin Zhang, Shijian Lu
To accommodate fair variation of plausible facial animations for the same audio, we design a transformer-based probabilistic mapping network that can model the variational facial animation distribution conditioned upon the input audio and autoregressively convert the audio signals into a facial animation sequence.
no code implementations • 18 Apr 2023 • Rongliang Wu, Yingchen Yu, Fangneng Zhan, Jiahui Zhang, Shengcai Liao, Shijian Lu
POCE achieves the more accessible and realistic pose-controllable expression editing by mapping face images into UV space, where facial expressions and head poses can be disentangled and edited separately.
no code implementations • CVPR 2023 • Kaiwen Cui, Yingchen Yu, Fangneng Zhan, Shengcai Liao, Shijian Lu1, Eric Xing
The first is aggregated generative KD that mitigates the discriminator overfitting by challenging the discriminator with harder learning tasks and distilling more generalizable knowledge from the pre-trained models.
1 code implementation • CVPR 2023 • Kunhao Liu, Fangneng Zhan, YiWen Chen, Jiahui Zhang, Yingchen Yu, Abdulmotaleb El Saddik, Shijian Lu, Eric Xing
In addition, it transforms the grid features according to the reference style which directly leads to high-quality zero-shot style transfer.
no code implementations • 4 Aug 2022 • Jiahui Zhang, Fangneng Zhan, Yingchen Yu, Rongliang Wu, Xiaoqin Zhang, Shijian Lu
In addition, stochastic noises fed to the generator are employed for unconditional detail generation, which tends to produce unfaithful details that compromise the fidelity of the generated SR image.
no code implementations • 21 Jul 2022 • Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Kaiwen Cui, Changgong Zhang, Shijian Lu
Extensive experiments over multiple conditional image generation tasks show that our method achieves superior diverse image generation performance qualitatively and quantitatively as compared with the state-of-the-art.
no code implementations • 6 Jul 2022 • Jiahui Zhang, Fangneng Zhan, Rongliang Wu, Yingchen Yu, Wenqing Zhang, Bai Song, Xiaoqin Zhang, Shijian Lu
With the feature transport plan as the guidance, a novel pose calibration technique is designed which rectifies the initially randomized camera poses by predicting relative pose transformations between the pair of rendered and real images.
1 code implementation • 6 Jul 2022 • Yingchen Yu, Fangneng Zhan, Rongliang Wu, Jiahui Zhang, Shijian Lu, Miaomiao Cui, Xuansong Xie, Xian-Sheng Hua, Chunyan Miao
In addition, we design a simple yet effective scheme that explicitly maps CLIP embeddings (of target text) to the latent space and fuses them with latent codes for effective latent code optimization and accurate editing.
no code implementations • CVPR 2022 • Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Changgong Zhang
We design a Marginal Contrastive Learning Network (MCL-Net) that explores contrastive learning to learn domain-invariant features for realistic exemplar-based image translation.
1 code implementation • CVPR 2022 • Fangneng Zhan, Jiahui Zhang, Yingchen Yu, Rongliang Wu, Shijian Lu
Perceiving the similarity between images has been a long-standing and fundamental problem underlying various visual generation tasks.
1 code implementation • CVPR 2022 • Gongjie Zhang, Zhipeng Luo, Yingchen Yu, Kaiwen Cui, Shijian Lu
First, it projects object queries into the same embedding space as encoded image features, where the matching can be accomplished efficiently with aligned semantics.
2 code implementations • 27 Dec 2021 • Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Lingjie Liu, Adam Kortylewski, Christian Theobalt, Eric Xing
With superb power in modeling the interaction among multimodal information, multimodal image synthesis and editing has become a hot research topic in recent years.
1 code implementation • ICCV 2021 • Yingchen Yu, Fangneng Zhan, Shijian Lu, Jianxiong Pan, Feiying Ma, Xuansong Xie, Chunyan Miao
This paper presents WaveFill, a wavelet-based inpainting network that decomposes images into multiple frequency bands and fills the missing regions in each frequency band separately and explicitly.
2 code implementations • 7 Jul 2021 • Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Kaiwen Cui, Aoran Xiao, Shijian Lu, Chunyan Miao
This paper presents a versatile image translation and manipulation framework that achieves accurate semantic and style guidance in image generation by explicitly building a correspondence.
no code implementations • 1 Jul 2021 • Jiahui Zhang, Shijian Lu, Fangneng Zhan, Yingchen Yu
Extensive experiments on synthetic datasets and real images show that the proposed CRL-SR can handle multi-modal and spatially variant degradation effectively under blind settings and it also outperforms state-of-the-art SR methods qualitatively and quantitatively.
no code implementations • CVPR 2021 • Fangneng Zhan, Yingchen Yu, Kaiwen Cui, Gongjie Zhang, Shijian Lu, Jianxiong Pan, Changgong Zhang, Feiying Ma, Xuansong Xie, Chunyan Miao
In addition, we design a semantic-activation normalization scheme that injects style features of exemplars into the image translation process successfully.
no code implementations • 26 Apr 2021 • Yingchen Yu, Fangneng Zhan, Rongliang Wu, Jianxiong Pan, Kaiwen Cui, Shijian Lu, Feiying Ma, Xuansong Xie, Chunyan Miao
With image-level attention, transformers enable to model long-range dependencies and generate diverse contents with autoregressive modeling of pixel-sequence distributions.
1 code implementation • 20 Feb 2021 • Fangneng Zhan, Yingchen Yu, Changgong Zhang, Rongliang Wu, WenBo Hu, Shijian Lu, Feiying Ma, Xuansong Xie, Ling Shao
This paper presents Geometric Mover's Light (GMLight), a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation.
no code implementations • 21 Dec 2020 • Fangneng Zhan, Changgong Zhang, Yingchen Yu, Yuan Chang, Shijian Lu, Feiying Ma, Xuansong Xie
Motivated by the Earth Mover distance, we design a novel spherical mover's loss that guides to regress light distribution parameters accurately by taking advantage of the subtleties of spherical distribution.