no code implementations • 11 Mar 2023 • Jiawei Huang, Akito Iizuka, Hajime Tanaka, Taku Komura, Yoshifumi Kitamura
The variance reduction speed of physically-based rendering is heavily affected by the adopted importance sampling technique.
no code implementations • 25 Nov 2022 • Xiaoxiao Long, Cheng Lin, Lingjie Liu, YuAn Liu, Peng Wang, Christian Theobalt, Taku Komura, Wenping Wang
In this paper, we propose to represent surfaces as the Unsigned Distance Function (UDF) and develop a new volume rendering scheme to learn the neural UDF representation.
no code implementations • 19 Nov 2022 • Zhiyang Dou, Qingxuan Wu, Cheng Lin, Zeyu Cao, Qiangqiang Wu, Weilin Wan, Taku Komura, Wenping Wang
In this paper, we introduce a set of simple yet effective TOken REduction (TORE) strategies for Transformer-based Human Mesh Recovery from monocular images.
1 code implementation • 20 Sep 2022 • Yilin Wen, Hao Pan, Lei Yang, Jia Pan, Taku Komura, Wenping Wang
Understanding dynamic hand motions and actions from egocentric RGB videos is a fundamental yet challenging task due to self-occlusion and ambiguity.
no code implementations • 10 Jul 2022 • Peng Wang, YuAn Liu, Guying Lin, Jiatao Gu, Lingjie Liu, Taku Komura, Wenping Wang
ProLiF encodes a 4D light field, which allows rendering a large batch of rays in one training step for image- or patch-level losses.
no code implementations • 27 Jun 2022 • Jiepeng Wang, Peng Wang, Xiaoxiao Long, Christian Theobalt, Taku Komura, Lingjie Liu, Wenping Wang
The key idea of NeuRIS is to integrate estimated normal of indoor scenes as a prior in a neural rendering framework for reconstructing large texture-less shapes and, importantly, to do this in an adaptive manner to also enable the reconstruction of irregular shapes with fine details.
no code implementations • 25 Jun 2022 • Weilin Wan, Lei Yang, Lingjie Liu, Zhuoying Zhang, Ruixing Jia, Yi-King Choi, Jia Pan, Christian Theobalt, Taku Komura, Wenping Wang
We also observe that an object's intrinsic physical properties are useful for the object motion prediction, and thus design a set of object dynamic descriptors to encode such intrinsic properties.
1 code implementation • 12 Jun 2022 • Xiaoxiao Long, Cheng Lin, Peng Wang, Taku Komura, Wenping Wang
We introduce SparseNeuS, a novel neural rendering based method for the task of surface reconstruction from multi-view images.
1 code implementation • 22 Apr 2022 • YuAn Liu, Yilin Wen, Sida Peng, Cheng Lin, Xiaoxiao Long, Taku Komura, Wenping Wang
In this paper, we present a generalizable model-free 6-DoF object pose estimator called Gen6D.
no code implementations • 12 Jan 2022 • Ian Mason, Sebastian Starke, Taku Komura
In this work we present a style modelling system that uses an animation synthesis network to model motion content based on local motion phases.
1 code implementation • CVPR 2022 • Yingruo Fan, Zhaojiang Lin, Jun Saito, Wenping Wang, Taku Komura
Speech-driven 3D facial animation is challenging due to the complex geometry of human faces and the limited availability of 3D audio-visual data.
no code implementations • 4 Dec 2021 • Yingruo Fan, Zhaojiang Lin, Jun Saito, Wenping Wang, Taku Komura
The existing datasets are collected to cover as many different phonemes as possible instead of sentences, thus limiting the capability of the audio-based model to learn more diverse contexts.
1 code implementation • 27 Jul 2021 • Yilin Wen, Xiangyu Li, Hao Pan, Lei Yang, Zheng Wang, Taku Komura, Wenping Wang
Scalable 6D pose estimation for rigid objects from RGB images aims at handling multiple objects and generalizing to novel objects.
4 code implementations • NeurIPS 2021 • Peng Wang, Lingjie Liu, YuAn Liu, Christian Theobalt, Taku Komura, Wenping Wang
In NeuS, we propose to represent a surface as the zero-level set of a signed distance function (SDF) and develop a new volume rendering method to train a neural SDF representation.
no code implementations • 22 Jun 2020 • Mingyi Shi, Kfir Aberman, Andreas Aristidou, Taku Komura, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen
We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video. While previous methods rely on either rigging or inverse kinematics (IK) to associate a consistent skeleton with temporally coherent joint rotations, our method is the first data-driven approach that directly outputs a kinematic skeleton, which is a complete, commonly used, motion representation.
1 code implementation • 10/06 2020 • Sebastian Dorothee Starke, Yiwei Zhao, Taku Komura, Kazi A. Zaman
Training a bipedal character to play basketball and interact with objects, or a quadruped character to move in various locomotion modes, are difficult tasks due to the fast and complex contacts happening during the motion.
no code implementations • 7 Feb 2020 • Chuanyu Yang, Kai Yuan, Wolfgang Merkt, Taku Komura, Sethu Vijayakumar, Zhibin Li
This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i. e., ankle, hip, foot tilting, and stepping strategies.
no code implementations • 13 Dec 2019 • Pengpeng Hu, Edmond SL Ho, Nauman Aslam, Taku Komura, Hubert PH Shum
However, the virtual clothing fit evaluation is still under-researched.
no code implementations • 16 Apr 2018 • Pengpeng Hu, Duan Li, Ge Wu, Taku Komura, Dongliang Zhang, Yueqi Zhong
A personalized mannequin is essential for apparel customization using CAD technologies.
no code implementations • 6 Nov 2017 • Pengpeng Hu, Taku Komura, Duan Li, Ge Wu, Yueqi Zhong
Purpose The purpose of this paper is to present a novel framework of reconstructing the 3D textile model with synthesized texture.
no code implementations • 9 May 2017 • Pengpeng Hu, Taku Komura, Daniel Holden, Yueqi Zhong
In this paper, we propose a novel scanning-based solution for modeling and animating characters wearing multiple layers of clothes.