Search Results for author: Taku Komura

Found 23 papers, 7 papers with code

Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians

no code implementations11 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.

NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologies

no code implementations25 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.

Neural Rendering

TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer

no code implementations19 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.

Human Mesh Recovery

Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action Recognition from Egocentric RGB Videos

1 code implementation20 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.

3D Hand Pose Estimation Action Recognition

Progressively-connected Light Field Network for Efficient View Synthesis

no code implementations10 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.

Novel View Synthesis

NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors

no code implementations27 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.

3D Reconstruction Neural Rendering

Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions

no code implementations25 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.

Human-Object Interaction Detection motion prediction

SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views

1 code implementation12 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.

Neural Rendering Surface Reconstruction

Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phases

no code implementations12 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.

Style Transfer

FaceFormer: Speech-Driven 3D Facial Animation with Transformers

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.

3D Face Animation

Joint Audio-Text Model for Expressive Speech-Driven 3D Facial Animation

no code implementations4 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.

Language Modelling

DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation

1 code implementation27 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.

6D Pose Estimation Metric Learning +2

NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction

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.

Novel View Synthesis Surface Reconstruction

MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency

no code implementations22 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.

Local motion phases for learning multi-contact character movements

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.

Learning Whole-body Motor Skills for Humanoids

no code implementations7 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.

Personalized 3D mannequin reconstruction based on 3D scanning

no code implementations16 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.

3D textile reconstruction based on KinectFusion and synthesized texture

no code implementations6 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.

Scanning and animating characters dressed in multiple-layer garments

no code implementations9 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.

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