no code implementations • 27 Mar 2024 • Sicheng Li, Keqiang Sun, Zhixin Lai, Xiaoshi Wu, Feng Qiu, Haoran Xie, Kazunori Miyata, Hongsheng Li
Secondly, to overcome the issue of limited conditional supervision, we introduce Diffusion Consistency Loss (DCL), which applies supervision on the denoised latent code at any given time step.
no code implementations • 21 Dec 2023 • Keqiang Sun, Dor Litvak, Yunzhi Zhang, Hongsheng Li, Jiajun Wu, Shangzhe Wu
We introduce Ponymation, a new method for learning a generative model of articulated 3D animal motions from raw, unlabeled online videos.
1 code implementation • ICCV 2023 • Jiawei Yao, Chuming Li, Keqiang Sun, Yingjie Cai, Hao Li, Wanli Ouyang, Hongsheng Li
Monocular 3D Semantic Scene Completion (SSC) has garnered significant attention in recent years due to its potential to predict complex semantics and geometry shapes from a single image, requiring no 3D inputs.
3D Semantic Scene Completion from a single 2D image 3D Semantic Scene Completion from a single RGB image
1 code implementation • 15 Jun 2023 • Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, Hongsheng Li
By fine-tuning CLIP on HPD v2, we obtain Human Preference Score v2 (HPS v2), a scoring model that can more accurately predict human preferences on generated images.
1 code implementation • ICCV 2023 • Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, Hongsheng Li
To address this issue, we collect a dataset of human choices on generated images from the Stable Foundation Discord channel.
no code implementations • 23 Nov 2022 • Keqiang Sun, Shangzhe Wu, Ning Zhang, Zhaoyang Huang, Quan Wang, Hongsheng Li
Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e. g., controlling the shapes, expressions, textures, and poses of the generated face images.
no code implementations • 16 Jun 2022 • Keqiang Sun, Shangzhe Wu, Zhaoyang Huang, Ning Zhang, Quan Wang, Hongsheng Li
Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e. g., controlling the shapes, expressions, textures, and poses of the generated face images.
no code implementations • CVPR 2021 • Jingtan Piao, Keqiang Sun, KwanYee Lin, Quan Wang, Hongsheng Li
Since the GAR learns to model the complicated real-world image, instead of relying on the simplified graphics rules, it is capable of producing realistic images, which essentially inhibits the domain-shift noise in training and optimization.
1 code implementation • ICCV 2019 • Keqiang Sun, Wayne Wu, Tinghao Liu, Shuo Yang, Quan Wang, Qiang Zhou, Zuochang Ye, Chen Qian
A structure predictor is proposed to predict the missing face structural information temporally, which serves as a geometry prior.
1 code implementation • ICCV 2019 • Shengju Qian, Keqiang Sun, Wayne Wu, Chen Qian, Jiaya Jia
Facial landmark detection, or face alignment, is a fundamental task that has been extensively studied.
Ranked #18 on Face Alignment on WFLW