no code implementations • 10 Jan 2024 • Zhaoxi Chen, Gyeongsik Moon, Kaiwen Guo, Chen Cao, Stanislav Pidhorskyi, Tomas Simon, Rohan Joshi, Yuan Dong, Yichen Xu, Bernardo Pires, He Wen, Lucas Evans, Bo Peng, Julia Buffalini, Autumn Trimble, Kevyn McPhail, Melissa Schoeller, Shoou-I Yu, Javier Romero, Michael Zollhöfer, Yaser Sheikh, Ziwei Liu, Shunsuke Saito
To simplify the personalization process while retaining photorealism, we build a powerful universal relightable prior based on neural relighting from multi-view images of hands captured in a light stage with hundreds of identities.
no code implementations • 9 Oct 2023 • Donglai Xiang, Fabian Prada, Zhe Cao, Kaiwen Guo, Chenglei Wu, Jessica Hodgins, Timur Bagautdinov
Clothing is an important part of human appearance but challenging to model in photorealistic avatars.
no code implementations • 10 Apr 2023 • Weisong Zhao, Xiangyu Zhu, Kaiwen Guo, Xiao-Yu Zhang, Zhen Lei
Therefore, we seek to probe the target logits to extract the primary knowledge related to face identity, and discard the others, to make the distillation more achievable for the student network.
no code implementations • 20 Jul 2022 • Edoardo Remelli, Timur Bagautdinov, Shunsuke Saito, Tomas Simon, Chenglei Wu, Shih-En Wei, Kaiwen Guo, Zhe Cao, Fabian Prada, Jason Saragih, Yaser Sheikh
To circumvent this, we propose a novel volumetric avatar representation by extending mixtures of volumetric primitives to articulated objects.
no code implementations • 11 Jul 2022 • Chaonan Ji, Tao Yu, Kaiwen Guo, Jingxin Liu, Yebin Liu
For the relighting, we introduce a ray tracing-based per-pixel lighting representation that explicitly models high-frequency shadows and propose a learning-based shading refinement module to restore realistic shadows (including hard cast shadows) from the ray-traced shading maps.
no code implementations • CVPR 2022 • Yuheng Jiang, Suyi Jiang, Guoxing Sun, Zhuo Su, Kaiwen Guo, Minye Wu, Jingyi Yu, Lan Xu
In this paper, we propose NeuralHOFusion, a neural approach for volumetric human-object capture and rendering using sparse consumer RGBD sensors.
no code implementations • CVPR 2021 • Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, Yebin Liu
Human volumetric capture is a long-standing topic in computer vision and computer graphics.
no code implementations • CVPR 2021 • Zhe Li, Tao Yu, Zerong Zheng, Kaiwen Guo, Yebin Liu
By contributing a novel reconstruction framework which contains pose-guided keyframe selection and robust implicit surface fusion, our method fully utilizes the advantages of both tracking-based methods and tracking-free inference methods, and finally enables the high-fidelity reconstruction of dynamic surface details even in the invisible regions.
1 code implementation • CVPR 2021 • Feitong Tan, Danhang Tang, Mingsong Dou, Kaiwen Guo, Rohit Pandey, Cem Keskin, Ruofei Du, Deqing Sun, Sofien Bouaziz, Sean Fanello, Ping Tan, yinda zhang
In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses.
no code implementations • CVPR 2021 • Xin Suo, Yuheng Jiang, Pei Lin, Yingliang Zhang, Kaiwen Guo, Minye Wu, Lan Xu
4D reconstruction and rendering of human activities is critical for immersive VR/AR experience. Recent advances still fail to recover fine geometry and texture results with the level of detail present in the input images from sparse multi-view RGB cameras.
no code implementations • ECCV 2018 • Zerong Zheng, Tao Yu, Hao Li, Kaiwen Guo, Qionghai Dai, Lu Fang, Yebin Liu
We propose a light-weight and highly robust real-time human performance capture method based on a single depth camera and sparse inertial measurement units (IMUs).
3 code implementations • ECCV 2018 • Shi Yan, Chenglei Wu, Lizhen Wang, Feng Xu, Liang An, Kaiwen Guo, Yebin Liu
Consumer depth sensors are more and more popular and come to our daily lives marked by its recent integration in the latest Iphone X.
no code implementations • CVPR 2018 • Tao Yu, Zerong Zheng, Kaiwen Guo, Jianhui Zhao, Qionghai Dai, Hao Li, Gerard Pons-Moll, Yebin Liu
We further propose a joint motion tracking method based on the double layer representation to enable robust and fast motion tracking performance.
no code implementations • ICCV 2017 • Tao Yu, Kaiwen Guo, Feng Xu, Yuan Dong, Zhaoqi Su, Jianhui Zhao, Jianguo Li, Qionghai Dai, Yebin Liu
To reduce the ambiguities of the non-rigid deformation parameterization on the surface graph nodes, we take advantage of the internal articulated motion prior for human performance and contribute a skeleton-embedded surface fusion (SSF) method.
no code implementations • 29 Oct 2016 • Lan Xu, Lu Fang, Wei Cheng, Kaiwen Guo, Guyue Zhou, Qionghai Dai, Yebin Liu
We propose a novel non-rigid surface registration method to track and fuse the depth of the three flying cameras for surface motion tracking of the moving target, and simultaneously calculate the pose of each flying camera.
no code implementations • ICCV 2015 • Kaiwen Guo, Feng Xu, Yangang Wang, Yebin Liu, Qionghai Dai
We present a new motion tracking method to robustly reconstruct non-rigid geometries and motions from single view depth inputs captured by a consumer depth sensor.