Search Results for author: Quankai Gao

Found 6 papers, 5 papers with code

GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation

no code implementations19 Mar 2024 Quankai Gao, Qiangeng Xu, Zhe Cao, Ben Mildenhall, Wenchao Ma, Le Chen, Danhang Tang, Ulrich Neumann

While the optimization can draw photometric reference from the input videos or be regulated by generative models, directly supervising Gaussian motions remains underexplored.

Novel View Synthesis Optical Flow Estimation

InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation

1 code implementation24 Sep 2023 Cho-Ying Wu, Quankai Gao, Chin-Cheng Hsu, Te-Lin Wu, Jing-Wen Chen, Ulrich Neumann

To facilitate our investigation for robustness and address limitations of previous works, we collect InSpaceType, a high-quality and high-resolution RGBD dataset for general indoor environments.

Indoor Monocular Depth Estimation Monocular Depth Estimation

Strivec: Sparse Tri-Vector Radiance Fields

1 code implementation ICCV 2023 Quankai Gao, Qiangeng Xu, Hao Su, Ulrich Neumann, Zexiang Xu

In contrast to TensoRF which uses a global tensor and focuses on their vector-matrix decomposition, we propose to utilize a cloud of local tensors and apply the classic CANDECOMP/PARAFAC (CP) decomposition to factorize each tensor into triple vectors that express local feature distributions along spatial axes and compactly encode a local neural field.

Tensor Decomposition

Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance

1 code implementation30 Aug 2022 Fariborz Taherkhani, Aashish Rai, Quankai Gao, Shaunak Srivastava, Xuanbai Chen, Fernando de la Torre, Steven Song, Aayush Prakash, Daeil Kim

3D face modeling has been an active area of research in computer vision and computer graphics, fueling applications ranging from facial expression transfer in virtual avatars to synthetic data generation.

3D Face Modelling Face Model +1

Deep Graph Matching under Quadratic Constraint

1 code implementation CVPR 2021 Quankai Gao, Fudong Wang, Nan Xue, Jin-Gang Yu, Gui-Song Xia

Recently, deep learning based methods have demonstrated promising results on the graph matching problem, by relying on the descriptive capability of deep features extracted on graph nodes.

Descriptive Graph Matching

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