Jianyuan Min

Google (Canada)

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Jinxiang Chai

Nanjing University

Sandra Skaff

Canon (United States)

George Bebis

University of Nevada, Reno

Daisuke Iwai

Osaka University

Richard Boyle

Oregon Health & Science University

Carlos Scheidegger

University of Arizona

Darko Koračin

University of Split

Fatih Porikli

Qualcomm (United Kingdom)

Amela Sadagić

Naval Postgraduate School

Tobias Isenberg

Centre National de la Recherche Scientifique

Cooperative Institutions

University of Nevada, Reno

Chinese Academy of Sciences

Tsinghua University

Beijing Institute of Technology

Lawrence Berkeley National Laboratory

Hefei University of Technology

Tsinghua–Berkeley Shenzhen Institute

State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation

Argonne National Laboratory

University of California, Riverside

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Video-based hand manipulation capture through composite motion control

ACM Transactions on Graphics (2013)

Yangang Wang Jianyuan Min Jianjie Zhang Yebin Liu Feng Xu

This paper describes a new method for acquiring physically realistic hand manipulation data from multiple video streams. The key idea of our approach is to introduce a composite motion control to simultaneously model hand articulation, object movement, and subtle interaction between the hand and object. We formulate video-based hand manipulation capture in an optimization framework by maximizing the consistency between the simulated motion and the observed image data. We search an optimal motion control that drives the simulation to best match the observed image data. We demonstrate the effectiveness of our approach by capturing a wide range of high-fidelity dexterous manipulation data. We show the power of our recovered motion controllers by adapting the captured motion data to new objects with different properties. The system achieves superior performance against alternative methods such as marker-based motion capture and kinematic hand motion tracking.

Motion Capture

Match moving

Tracking (education)

10.1145/2461912.2462000

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Citations (82)

Advances in Visual Computing: Proceedings of the 12th International Symposium (ISVC, December 12–14, Las Vegas, NV, USA), Part 2

HAL (Le Centre pour la Communication Scientifique Directe) (2016)

George Bebis Richard Boyle Bahram Parvin Darko Koračin Fatih Porikli

Las vegas

10.1007/978-3-319-50832-0

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Citations (10)

Intuitive generation of realistic motions for articulated human characters

Jianyuan Min

Source

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Citations (0)

Variable Duration Motion Texture for Human Motion Modeling

Springer eBooks (2006)

Tianyu Huang Fengxia Li Zhan Shou-yi Jianyuan Min

Structure from Motion

Texture (cosmology)

Human motion

Linear motion

Motion field

Motion analysis

10.1007/11801603_64

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Citations (3)

Advances in Visual Computing

Lecture notes in computer science (2016)

George Bebis Richard Boyle Bahram Parvin Darko Koračin Fatih Porikli

Las vegas

10.1007/978-3-319-50832-0

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Citations (23)

Synthesis and editing of personalized stylistic human motion.

Jianyuan Min Huajun Liu Jinxiang Chai

This paper presents a generative human motion model for synthesis, retargeting, and editing of personalized human motion styles. We first record a human motion database from multiple actors performing a wide variety of motion styles for particular actions. We then apply multilinear analysis techniques to construct a generative motion model of the form x = g(a, e) for particular human actions, where the parameters a and e control and variations of the motion x respectively. The new modular representation naturally supports motion generalization to new actors and/or styles. We demonstrate the power and flexibility of the multilinear motion models by synthesizing personalized stylistic human motion and transferring the stylistic motions from one actor to another. We also show the effectiveness of our model by editing stylistic motion in style and/or identity space.

Retargeting

Representation

Motion Capture

Generative model

Human motion

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Citations (0)

Physically valid statistical models for human motion generation

ACM Transactions on Graphics (2011)

Xiaolin Wei Jianyuan Min Jinxiang Chai

This article shows how statistical motion priors can be combined seamlessly with physical constraints for human motion modeling and generation. The key idea of the approach is to learn a nonlinear probabilistic force field function from prerecorded motion data with Gaussian processes and combine it with physical constraints in a probabilistic framework. In addition, we show how to effectively utilize the new model to generate a wide range of natural-looking motions that achieve the goals specified by users. Unlike previous statistical motion models, our model can generate physically realistic animations that react to external forces or changes in physical quantities of human bodies and interaction environments. We have evaluated the performance of our system by comparing against ground-truth motion data and alternative methods.

Motion Capture

10.1145/1966394.1966398

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Citations (53)

Motion graphs++

ACM Transactions on Graphics (2012)

Jianyuan Min Jinxiang Chai

This paper introduces a new generative statistical model that allows for human motion analysis and synthesis at both semantic and kinematic levels. Our key idea is to decouple complex variations of human movements into finite structural variations and continuous style variations and encode them with a concatenation of morphable functional models. This allows us to model not only a rich repertoire of behaviors but also an infinite number of style variations within the same action. Our models are appealing for motion analysis and synthesis because they are highly structured, contact aware , and semantic embedding . We have constructed a compact generative motion model from a huge and heterogeneous motion database (about two hours mocap data and more than 15 different actions). We have demonstrated the power and effectiveness of our models by exploring a wide variety of applications, ranging from automatic motion segmentation, recognition, and annotation, and online/offline motion synthesis at both kinematics and behavior levels to semantic motion editing. We show the superiority of our model by comparing it with alternative methods.

Motion Capture

Concatenation (mathematics)

Generative model

Motion analysis

ENCODE

Retargeting

10.1145/2366145.2366172

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Citations (185)

Flexible registration of human motion data with parameterized motion models

Yen‐Lin Chen Jianyuan Min Jinxiang Chai

This paper presents an efficient model-based approach for automatic human motion registration, which builds temporal correspondences between structurally similar but distinctive motion examples. The key idea of the model-based registration process is to construct a parameterized motion model from a set of preregistered motion examples. With such a model, we can register an input motion with the parameterized motion model by continuously deforming the model to best match the input motion. We formulate the registration process in a gradient-based nonlinear optimization framework by minimizing an objective function that measures differences between the input motion and deforming motion. We also develop a multi-resolution optimization process to efficiently estimate the model parameters as well as the temporal correspondences between the input motion and deforming motion. We demonstrate the performance of our approach by testing the algorithm on difficult motion sequences and comparing with alternative approaches.

Motion Capture

Motion field

Structure from Motion

Quarter-pixel motion

10.1145/1507149.1507180

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Citations (7)

Synthesis and editing of personalized stylistic human motion

Jianyuan Min Huajun Liu Jinxiang Chai

This paper presents a generative human motion model for synthesis, retargeting, and editing of personalized human motion styles. We first record a human motion database from multiple actors performing a wide variety of motion styles for particular actions. We then apply multilinear analysis techniques to construct a generative motion model of the form x = g(a, e) for particular human actions, where the parameters a and e control "identity" and "style" variations of the motion x respectively. The new modular representation naturally supports motion generalization to new actors and/or styles. We demonstrate the power and flexibility of the multilinear motion models by synthesizing personalized stylistic human motion and transferring the stylistic motions from one actor to another. We also show the effectiveness of our model by editing stylistic motion in style and/or identity space.

Retargeting

Representation

Generative model

Motion Capture

Human motion

10.1145/1730804.1730811

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Citations (78)