Embodied Hands: Modeling and Capturing Hands and Bodies Together

Abstract: Humans move their hands and bodies together to communicate and solve tasks. Capturing and replicating such coordinated activity is critical for virtual characters that behave realistically. Surprisingly, most methods treat the 3D modeling and tracking of bodies and hands separately. Here we formulate a model of hands and bodies interacting together and fit it to full-body 4D sequences. When scanning or capturing the full body in 3D, hands are small and often partially occluded, making their shape and pose hard to recover. To cope with low-resolution, occlusion, and noise, we develop a new model called MANO (hand Model with Articulated and Non-rigid defOrmations). MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses. The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand. MANO provides a compact mapping from hand poses to pose blend shape corrections and a linear manifold of pose synergies. We attach MANO to a standard parameterized 3D body shape model (SMPL), resulting in a fully articulated body and hand model (SMPL+H). We illustrate SMPL+H by fitting complex, natural, activities of subjects captured with a 4D scanner. The fitting is fully automatic and results in full body models that move naturally with detailed hand motions and a realism not seen before in full body performance capture. The models and data are freely available for research purposes in our website (http://mano.is.tue.mpg.de).

• The paper introduces SMPL+H, merging the SMPL body model with the MANO hand model to capture coordinated hand and body movements.
• It leverages high-resolution 4D scans and pose-dependent deformations to accurately model intricate hand motions despite occlusions.
• The publicly available model and datasets empower advancements in VR, animation, and human motion research.

Overview of "Embodied Hands: Modeling and Capturing Hands and Bodies Together"

"Embodied Hands: Modeling and Capturing Hands and Bodies Together" by Romero et al. presents a detailed methodology for the joint modeling and capturing of human body and hand movements through a new model called SMPL+H. This model combines the statistical model of the human body (SMPL) with a novel data-driven hand model named MANO, facilitating a comprehensive, articulated representation crucial for applications in virtual reality, computer graphics, and performance capture.

The paper identifies a significant shortfall in existing body models—namely, the inability to accurately capture and reproduce the intricate motions of hands and their interaction with the human body. The authors overcome this challenge by jointly modeling the entire body and hands. MANO, an acronym for 'hand Model with Articulated and Non-rigid defOrmations', extends the SMPL body model with articulated hand movements obtained from high-resolution scans. This combination supports the creation of avatars that can perform coordinated hand and body movements, improving realism in virtual contexts.

Key Contributions and Methodology

1. High-Resolution Hand Model (MANO): MANO is constructed from approximately 1000 high-fidelity scans, capturing the detailed nuances of hand poses by incorporating pose-dependent deformations. These scans originate from 31 subjects, allowing the model to generalize across different hand shapes and movements. MANO is unique for its low-dimensional, realistic representation and integration capabilities with existing body models.
2. Integration with Body Model (SMPL+H): The authors extend the SMPL model by integrating MANO to encompass body-hand interactions under varying conditions of occlusion and noise. The collective model captures intricate hand-body dynamics in a unified framework, which can be critical for domains requiring precise capturing of human motion.
3. Application and Data Capture: To validate SMPL+H, the researchers developed an approach for capturing full-body and hand sequences using a 4D scanner. This method, although responsive to environmental noise and occlusion, successfully illustrated the model's application in capturing synchronized, complex activities, which are often missed by conventional approaches.
4. Public Availability: The authors commit to advancing the research community by making their model along with the training datasets available for research purposes. This could stimulate further research and application across interdisciplinary fields.

Implications and Future Work

The implications of this research are considerable for enhancing realism in character animation and virtual reality systems, where previous models isolated hand tracking from body motion. The possibility of creating more interactive and believable digital avatars paves the way for advancements in gaming, simulations, and human-computer interaction.

One of the study's bold assertions is the capability to handle substantial noise and occlusion, which remains a critical challenge for real-time applications. Future endeavors could focus on real-time implementation, potentially leveraging deep learning frameworks to improve accuracy and computational efficiency. The scope of this work may also extend towards more integrated hand-object interaction modeling, enhancing the realism in environments where such interactions are pivotal.

In sum, the paper outlines a significant step forward in the realistic modeling of human figures, with the potential to redefine applications in fields requiring high fidelity human motion representations. The integration of detailed hand dynamics with full body modeling represents a pivotal stride towards achieving full autonomous human figure representation in computational environments.