Search Results for author: Zhe Cao

Found 12 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

Egocentric Whole-Body Motion Capture with FisheyeViT and Diffusion-Based Motion Refinement

no code implementations28 Nov 2023 Jian Wang, Zhe Cao, Diogo Luvizon, Lingjie Liu, Kripasindhu Sarkar, Danhang Tang, Thabo Beeler, Christian Theobalt

In this work, we explore egocentric whole-body motion capture using a single fisheye camera, which simultaneously estimates human body and hand motion.

 Ranked #1 on Egocentric Pose Estimation on GlobalEgoMocap Test Dataset (using extra training data)

Egocentric Pose Estimation Hand Detection +2

On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective

1 code implementation24 Dec 2022 Ying Wen, Ziyu Wan, Ming Zhou, Shufang Hou, Zhe Cao, Chenyang Le, Jingxiao Chen, Zheng Tian, Weinan Zhang, Jun Wang

The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making (IDM) systems.

Decision Making Image Captioning +2

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.

Learning Independent Object Motion from Unlabelled Stereoscopic Videos

no code implementations CVPR 2019 Zhe Cao, Abhishek Kar, Christian Haene, Jitendra Malik

Unlike prior learning based work which has focused on predicting dense pixel-wise optical flow field and/or a depth map for each image, we propose to predict object instance specific 3D scene flow maps and instance masks from which we are able to derive the motion direction and speed for each object instance.

Object Optical Flow Estimation

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