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Biomechanics And Motor Control Of Human Movement

3D models provide a common ground for different representations of human bodies. In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits in-the-wild. However, depending on the level of detail, it can be hard to impossible to acquire labeled data for training 2D estimators on large scale. We propose a hybrid approach to this problem: with an extended version of the recently introduced SMPLify method, we obtain high quality 3D body model fits for multiple human pose datasets. Human annotators solely sort good and bad fits. This procedure leads to an initial dataset, UP-3D, with rich annotations. With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body. Using the 91 landmark pose estimator, we present state-of-the art results for 3D human pose and shape estimation using an order of magnitude less training data and without assumptions about gender or pose in the fitting procedure. We show that UP-3D can be enhanced with these improved fits to grow in quantity and quality, which makes the system deployable on large scale. The data, code and models are available for research purposes.

/pdf/unite-the-people-closing-the-loop-between-3d-and-2d-human-1ofwt6yoql.pdf

Unite the People: Closing the Loop Between 3D and 2D Human Representations

Large datasets are the cornerstone of recent advances in computer vision using deep learning. In contrast, existing human motion capture (mocap) datasets are small and the motions limited, hampering progress on learning models of human motion. While there are many different datasets available, they each use a different parameterization of the body, making it difficult to integrate them into a single meta dataset. To address this, we introduce AMASS, a large and varied database of human motion that unifies 15 different optical marker-based mocap datasets by representing them within a common framework and parameterization. We achieve this using a new method, MoSh++, that converts mocap data into realistic 3D human meshes represented by a rigged body model. Here we use SMPL [Loper et al., 2015], which is widely used and provides a standard skeletal representation as well as a fully rigged surface mesh. The method works for arbitrary marker sets, while recovering soft-tissue dynamics and realistic hand motion. We evaluate MoSh++ and tune its hyperparameters using a new dataset of 4D body scans that are jointly recorded with markerbased mocap. The consistent representation of AMASS makes it readily useful for animation, visualization, and generating training data for deep learning. Our dataset is significantly richer than previous human motion collections, having more than 40 hours of motion data, spanning over 300 subjects, more than 11000 motions, and will be publicly available to the research community.

/pdf/amass-archive-of-motion-capture-as-surface-shapes-4301lyij8c.pdf

AMASS: Archive of Motion Capture As Surface Shapes

Large datasets are the cornerstone of recent advances in computer vision using deep learning In contrast, existing human motion capture (mocap) datasets are small and the motions limited, hampering progress on learning models of human motion While there are many different datasets available, they each use a different parameterization of the body, making it difficult to integrate them into a single meta dataset To address this, we introduce AMASS, a large and varied database of human motion that unifies 15 different optical marker-based mocap datasets by representing them within a common framework and parameterization We achieve this using a new method, MoSh++, that converts mocap data into realistic 3D human meshes represented by a rigged body model; here we use SMPL [doi:101145/28167952818013], which is widely used and provides a standard skeletal representation as well as a fully rigged surface mesh The method works for arbitrary marker sets, while recovering soft-tissue dynamics and realistic hand motion We evaluate MoSh++ and tune its hyperparameters using a new dataset of 4D body scans that are jointly recorded with marker-based mocap The consistent representation of AMASS makes it readily useful for animation, visualization, and generating training data for deep learning Our dataset is significantly richer than previous human motion collections, having more than 40 hours of motion data, spanning over 300 subjects, more than 11,000 motions, and will be publicly available to the research community

AMASS: Archive of Motion Capture as Surface Shapes

We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone. Our approach, dubbed the Dropout Autoencoder LSTM, is capable of synthesizing natural looking motion sequences over long time horizons without catastrophic drift or motion degradation. The model consists of two components, a 3-layer recurrent neural network to model temporal aspects and a novel auto-encoder that is trained to implicitly recover the spatial structure of the human skeleton via randomly removing information about joints during training time. This Dropout Autoencoder (D-AE) is then used to filter each predicted pose of the LSTM, reducing accumulation of error and hence drift over time. Furthermore, we propose new evaluation protocols to assess the quality of synthetic motion sequences even for which no ground truth data exists. The proposed protocols can be used to assess generated sequences of arbitrary length. Finally, we evaluate our proposed method on two of the largest motion-capture datasets available to date and show that our model outperforms the state-of-the-art on a variety of actions, including cyclic and acyclic motion, and that it can produce natural looking sequences over longer time horizons than previous methods.

Learning Human Motion Models for Long-term Predictions

We present a large-scale whole-body human motion database consisting of captured raw motion data as well as the corresponding post-processed motions. This database serves as a key element for a wide variety of research questions related e.g. to human motion analysis, imitation learning, action recognition and motion generation in robotics. In contrast to previous approaches, the motion data in our database considers the motions of the observed human subject as well as the objects with which the subject is interacting. The information about human-object relations is crucial for the proper understanding of human actions and their goal-directed reproduction on a robot. To facilitate the creation and processing of human motion data, we propose procedures and techniques for capturing of motion, labeling and organization of the motion capture data based on a Motion Description Tree, as well as for the normalization of human motion to an unified representation based on a reference model of the human body. We provide software tools and interfaces to the database allowing access and efficient search with the proposed motion representation.

/pdf/the-kit-whole-body-human-motion-database-mfgq9ggts0.pdf

The KIT whole-body human motion database

Large-scale human motion databases are key for research questions ranging from human motion analysis and synthesis, biomechanics of human motion, data-driven learning of motion primitives, and rehabilitation robotics to the design of humanoid robots and wearable robots such as exoskeletons. In this paper we present a large-scale database of whole-body human motion with methods and tools, which allows a unifying representation of captured human motion, and efficient search in the database, as well as the transfer of subject-specific motions to robots with different embodiments. To this end, captured subject-specific motion is normalized regarding the subject's height and weight by using a reference kinematics and dynamics model of the human body, the master motor map (MMM). In contrast with previous approaches and human motion databases, the motion data in our database consider not only the motions of the human subject but the position and motion of objects with which the subject is interacting as well. In addition to the description of the MMM reference model, we present procedures and techniques for the systematic recording, labeling, and organization of human motion capture data, object motions as well as the subject–object relations. To allow efficient search for certain motion types in the database, motion recordings are manually annotated with motion description tags organized in a tree structure. We demonstrate the transfer of human motion to humanoid robots and provide several examples of motion analysis using the database.

Unifying Representations and Large-Scale Whole-Body Motion Databases for Studying Human Motion

Robots, especially humanoids, are expected to perform human-like actions and adapt to our ways of communication in order to facilitate their acceptance in human society. Among humans, rules of communication change depending on background culture: greetings are a part of communication in which cultural differences are strong. Robots should adapt to these specific differences in order to communicate effectively, being able to select the appropriate manner of greeting for different cultures depending on the social context. In this paper, we present the modelling of social factors that influence greeting choice, and the resulting novel culture-dependent greeting gesture and words selection system. An experiment with German participants was run using the humanoid robot ARMAR-IIIb. Thanks to this system, the robot, after interacting with Germans, can perform greeting gestures appropriate to German culture in addition to a repertoire of greetings appropriate to Japanese culture.

https://journals.sagepub.com/doi/pdf/10.5772/60117

A Novel Greeting Selection System for a Culture-Adaptive Humanoid Robot:

Humanoid robots are expected to be able to communicate with humans using physical interaction, including hug, which is a common gesture of affection. In order to achieve that, their physical embodiment has to be carefully planned, as a user-friendly design will facilitate interaction and minimise repulsion. In this paper, we investigate the effect of manipulating the visual/tactile appearance of a robot, covering wires and metallic parts with clothes, and the auditory effect by enabling or disabling the connector of the hand. The experiment consists in a hugging interaction between the participants and the humanoid robot ARMAR-IIIb. Results after participation of 24 subjects confirm the positive effect from using clothes to modify the appearance and the negative effect of noise and vibration.

/pdf/is-hugging-a-robot-weird-investigating-the-influence-of-4t27mdhcih.pdf

Is hugging a robot weird? Investigating the influence of robot appearance on users' perception of hugging

In human-robot interaction, it is important for the robots to adapt to our ways of communication. As humans, rules of non-verbal communication, including greetings, change depending on our culture. Social robots should adapt to these specific differences in order to communicate effectively, as a correct way of approaching often results into better acceptance of the robot. In this study, a novel greeting gesture selection system is presented and an experiment is run using the robot ARMAR-IIIb. The robot performs greeting gestures appropriate to Japanese culture; after interacting with German participants, the selection should become appropriate to German culture. Results show that the mapping of gesture selection evolves successfully.

/pdf/a-novel-culture-dependent-gesture-selection-system-for-a-2ifu3cody0.pdf

Omer Terlemez

Papers

The KIT whole-body human motion database

Unifying Representations and Large-Scale Whole-Body Motion Databases for Studying Human Motion

A Novel Greeting Selection System for a Culture-Adaptive Humanoid Robot:

Is hugging a robot weird? Investigating the influence of robot appearance on users' perception of hugging

A novel culture-dependent gesture selection system for a humanoid robot performing greeting interaction