Search Results for author: Chenglei Wu

Found 17 papers, 5 papers with code

Diffusion Shape Prior for Wrinkle-Accurate Cloth Registration

no code implementations10 Nov 2023 Jingfan Guo, Fabian Prada, Donglai Xiang, Javier Romero, Chenglei Wu, Hyun Soo Park, Takaaki Shiratori, Shunsuke Saito

Registering clothes from 4D scans with vertex-accurate correspondence is challenging, yet important for dynamic appearance modeling and physics parameter estimation from real-world data.

Neural Strands: Learning Hair Geometry and Appearance from Multi-View Images

no code implementations28 Jul 2022 Radu Alexandru Rosu, Shunsuke Saito, Ziyan Wang, Chenglei Wu, Sven Behnke, Giljoo Nam

Furthermore, we introduce a novel neural rendering framework based on rasterization of the learned hair strands.

Neural Rendering

Drivable Volumetric Avatars using Texel-Aligned Features

no code implementations20 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.

Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing

no code implementations30 Jun 2022 Donglai Xiang, Timur Bagautdinov, Tuur Stuyck, Fabian Prada, Javier Romero, Weipeng Xu, Shunsuke Saito, Jingfan Guo, Breannan Smith, Takaaki Shiratori, Yaser Sheikh, Jessica Hodgins, Chenglei Wu

The key idea is to introduce a neural clothing appearance model that operates on top of explicit geometry: at training time we use high-fidelity tracking, whereas at animation time we rely on physically simulated geometry.

Modeling Clothing as a Separate Layer for an Animatable Human Avatar

no code implementations28 Jun 2021 Donglai Xiang, Fabian Prada, Timur Bagautdinov, Weipeng Xu, Yuan Dong, He Wen, Jessica Hodgins, Chenglei Wu

To address these difficulties, we propose a method to build an animatable clothed body avatar with an explicit representation of the clothing on the upper body from multi-view captured videos.

Inverse Rendering

Driving-Signal Aware Full-Body Avatars

no code implementations21 May 2021 Timur Bagautdinov, Chenglei Wu, Tomas Simon, Fabian Prada, Takaaki Shiratori, Shih-En Wei, Weipeng Xu, Yaser Sheikh, Jason Saragih

The core intuition behind our method is that better drivability and generalization can be achieved by disentangling the driving signals and remaining generative factors, which are not available during animation.

Imputation

MonoClothCap: Towards Temporally Coherent Clothing Capture from Monocular RGB Video

no code implementations22 Sep 2020 Donglai Xiang, Fabian Prada, Chenglei Wu, Jessica Hodgins

A differentiable renderer is utilized to align our captured shapes to the input frames by minimizing the difference in both silhouette, segmentation, and texture.

Surface Reconstruction valid

Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning

no code implementations24 Oct 2019 Xin Yao, Tianchi Huang, Chenglei Wu, Rui-Xiao Zhang, Lifeng Sun

Extensive experiments in several typical lifelong learning scenarios demonstrate that our method outperforms the state-of-the-art methods in both accuracies on new tasks and performance preservation on old tasks.

Continual Learning Image Classification +1

Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs

2 code implementations16 Aug 2019 Xin Yao, Tianchi Huang, Chenglei Wu, Rui-Xiao Zhang, Lifeng Sun

Federated learning (FL) enables on-device training over distributed networks consisting of a massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) devices.

Federated Learning

Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning

1 code implementation6 Aug 2019 Tianchi Huang, Chao Zhou, Rui-Xiao Zhang, Chenglei Wu, Xin Yao, Lifeng Sun

Using trace-driven and real-world experiments, we demonstrate significant improvements of Comyco's sample efficiency in comparison to prior work, with 1700x improvements in terms of the number of samples required and 16x improvements on training time required.

Imitation Learning

DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs

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.

Denoising

Learning Patch Reconstructability for Accelerating Multi-View Stereo

no code implementations CVPR 2018 Alex Poms, Chenglei Wu, Shoou-I Yu, Yaser Sheikh

By prioritizing stereo matching on a subset of patches that are highly reconstructable and also cover the 3D surface, we are able to accelerate MVS with minimal reduction in accuracy and completeness.

Stereo Matching Stereo Matching Hand +1

Modeling Facial Geometry Using Compositional VAEs

no code implementations CVPR 2018 Timur Bagautdinov, Chenglei Wu, Jason Saragih, Pascal Fua, Yaser Sheikh

We propose a method for learning non-linear face geometry representations using deep generative models.

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