Locomotion-Action-Manipulation: Synthesizing Human-Scene Interactions in Complex 3D Environments

Synthesizing interaction-involved human motions has been challenging due to the high complexity of 3D environments and the diversity of possible human behaviors within. We present LAMA, Locomotion-Action-MAnipulation, to synthesize natural and plausible long-term human movements in complex indoor environments. The key motivation of LAMA is to build a unified framework to encompass a series of everyday motions including locomotion, scene interaction, and object manipulation. Unlike existing methods that require motion data "paired" with scanned 3D scenes for supervision, we formulate the problem as a test-time optimization by using human motion capture data only for synthesis. LAMA leverages a reinforcement learning framework coupled with a motion matching algorithm for optimization, and further exploits a motion editing framework via manifold learning to cover possible variations in interaction and manipulation. Throughout extensive experiments, we demonstrate that LAMA outperforms previous approaches in synthesizing realistic motions in various challenging scenarios. Project page: https://jiyewise.github.io/projects/LAMA/ .Comment: Accepted to ICCV 202

A smart environment for biometric capture

The development of large scale biometric systems require experiments to be performed on large amounts of data. Existing capture systems are designed for fixed experiments and are not easily scalable. In this scenario even the addition of extra data is difficult. We developed a prototype biometric tunnel for the capture of non-contact biometrics. It is self contained and autonomous. Such a configuration is ideal for building access or deployment in secure environments. The tunnel captures cropped images of the subject's face and performs a 3D reconstruction of the person's motion which is used to extract gait information. Interaction between the various parts of the system is performed via the use of an agent framework. The design of this system is a trade-off between parallel and serial processing due to various hardware bottlenecks. When tested on a small population the extracted features have been shown to be potent for recognition. We currently achieve a moderate throughput of approximate 15 subjects an hour and hope to improve this in the future as the prototype becomes more complete

Synthesizing Diverse Human Motions in 3D Indoor Scenes

We present a novel method for populating 3D indoor scenes with virtual humans that can navigate the environment and interact with objects in a realistic manner. Existing approaches rely on high-quality training sequences that capture a diverse range of human motions in 3D scenes. However, such motion data is costly, difficult to obtain and can never cover the full range of plausible human-scene interactions in complex indoor environments. To address these challenges, we propose a reinforcement learning-based approach to learn policy networks that predict latent variables of a powerful generative motion model that is trained on a large-scale motion capture dataset (AMASS). For navigating in a 3D environment, we propose a scene-aware policy training scheme with a novel collision avoidance reward function. Combined with the powerful generative motion model, we can synthesize highly diverse human motions navigating 3D indoor scenes, meanwhile effectively avoiding obstacles. For detailed human-object interactions, we carefully curate interaction-aware reward functions by leveraging a marker-based body representation and the signed distance field (SDF) representation of the 3D scene. With a number of important training design schemes, our method can synthesize realistic and diverse human-object interactions (e.g.,~sitting on a chair and then getting up) even for out-of-distribution test scenarios with different object shapes, orientations, starting body positions, and poses. Experimental results demonstrate that our approach outperforms state-of-the-art human-scene interaction synthesis frameworks in terms of both motion naturalness and diversity. Video results are available on the project page: https://zkf1997.github.io/DIMOS

{MoCapDeform}: {M}onocular {3D} Human Motion Capture in Deformable Scenes

3D human motion capture from monocular RGB images respecting interactions ofa subject with complex and possibly deformable environments is a verychallenging, ill-posed and under-explored problem. Existing methods address itonly weakly and do not model possible surface deformations often occurring whenhumans interact with scene surfaces. In contrast, this paper proposesMoCapDeform, i.e., a new framework for monocular 3D human motion capture thatis the first to explicitly model non-rigid deformations of a 3D scene forimproved 3D human pose estimation and deformable environment reconstruction.MoCapDeform accepts a monocular RGB video and a 3D scene mesh aligned in thecamera space. It first localises a subject in the input monocular video alongwith dense contact labels using a new raycasting based strategy. Next, ourhuman-environment interaction constraints are leveraged to jointly optimiseglobal 3D human poses and non-rigid surface deformations. MoCapDeform achievessuperior accuracy than competing methods on several datasets, including ournewly recorded one with deforming background scenes.<br

Passive Control Architecture for Virtual Humans

International audienceIn the present paper, we introduce a new control architecture aimed at driving virtual humans in interaction with virtual environments, by motion capture. It brings decoupling of functionalities, and also of stability thanks to passivity. We show projections can break passivity, and thus must be used carefully. Our control scheme enables task space and internal control, contact, and joint limits management. Thanks to passivity, it can be easily extended. Besides, we introduce a new tool as for manikin's control, which makes it able to build passive projections, so as to guide the virtual manikin when sharp movements are needed

Lessons from digital puppetry - Updating a design framework for a perceptual user interface

While digital puppeteering is largely used just to augment full body motion capture in digital production, its technology and traditional concepts could inform a more naturalized multi-modal human computer interaction than is currently used with the new perceptual systems such as Kinect. Emerging immersive social media networks with their fully live virtual or augmented environments and largely inexperienced users would benefit the most from this strategy. This paper intends to define digital puppeteering as it is currently understood, and summarize its broad shortcomings based on expert evaluation. Based on this evaluation it will suggest updates and experiments using current perceptual technology and concepts in cognitive processing for existing human computer interaction taxonomy. This updated framework may be more intuitive and suitable in developing extensions to an emerging perceptual user interface for the general public

Human Factors Virtual Analysis Techniques for NASA's Space Launch System Ground Support using MSFC's Virtual Environments Lab (VEL)

Using virtual environments to assess complex large scale human tasks provides timely and cost effective results to evaluate designs and to reduce operational risks during assembly and integration of the Space Launch System (SLS). NASA's Marshall Space Flight Center (MSFC) uses a suite of tools to conduct integrated virtual analysis during the design phase of the SLS Program. Siemens Jack is a simulation tool that allows engineers to analyze human interaction with CAD designs by placing a digital human model into the environment to test different scenarios and assess the design's compliance to human factors requirements. Engineers at MSFC are using Jack in conjunction with motion capture and virtual reality systems in MSFC's Virtual Environments Lab (VEL). The VEL provides additional capability beyond standalone Jack to record and analyze a person performing a planned task to assemble the SLS at Kennedy Space Center (KSC). The VEL integrates Vicon Blade motion capture system, Siemens Jack, Oculus Rift, and other virtual tools to perform human factors assessments. By using motion capture and virtual reality, a more accurate breakdown and understanding of how an operator will perform a task can be gained. By virtual analysis, engineers are able to determine if a specific task is capable of being safely performed by both a 5% (approx. 5ft) female and a 95% (approx. 6'1) male. In addition, the analysis will help identify any tools or other accommodations that may to help complete the task. These assessments are critical for the safety of ground support engineers and keeping launch operations on schedule. Motion capture allows engineers to save and examine human movements on a frame by frame basis, while virtual reality gives the actor (person performing a task in the VEL) an immersive view of the task environment. This presentation will discuss the need of human factors for SLS and the benefits of analyzing tasks in NASA MSFC's VEL