MACS: Mass Conditioned 3D Hand and Object Motion Synthesis

Abstract: The physical properties of an object, such as mass, significantly affect how we manipulate it with our hands. Surprisingly, this aspect has so far been neglected in prior work on 3D motion synthesis. To improve the naturalness of the synthesized 3D hand object motions, this work proposes MACS the first MAss Conditioned 3D hand and object motion Synthesis approach. Our approach is based on cascaded diffusion models and generates interactions that plausibly adjust based on the object mass and interaction type. MACS also accepts a manually drawn 3D object trajectory as input and synthesizes the natural 3D hand motions conditioned by the object mass. This flexibility enables MACS to be used for various downstream applications, such as generating synthetic training data for ML tasks, fast animation of hands for graphics workflows, and generating character interactions for computer games. We show experimentally that a small-scale dataset is sufficient for MACS to reasonably generalize across interpolated and extrapolated object masses unseen during the training. Furthermore, MACS shows moderate generalization to unseen objects, thanks to the mass-conditioned contact labels generated by our surface contact synthesis model ConNet. Our comprehensive user study confirms that the synthesized 3D hand-object interactions are highly plausible and realistic.

• The paper presents MACS, a cascaded diffusion model that synthesizes 3D hand and object interactions by conditioning on object mass.
• It employs HandDiff and TrajDiff to generate natural hand gestures and object trajectories using a Gaussian denoising process.
• Empirical results show improved realism and generalizability over baseline models, benefiting VR, robotics, and interactive applications.

Mass Conditioned 3D Hand and Object Motion Synthesis: A Technical Overview

The paper under discussion introduces a novel approach named MACS, which addresses the synthesis of 3D hand and object motion considering the mass of the objects involved. The influence of an object's mass on how it is manipulated has typically been overlooked in the field of 3D motion synthesis, making the contributions of this work pertinent to advancing more realistic interactions in virtual environments.

Technical Contributions and Methodology

MACS employs a cascaded diffusion model to generate hand and object interactions that are conditioned on both the object's mass and the type of interaction. This model is notable for its flexibility, accepting manually drawn 3D object trajectories as input and subsequently generating natural hand motions that reflect the given object's mass.

Key technical components of the MACS framework include:

1. HandDiff and TrajDiff Models: HandDiff synthesizes hand joints and contacts, while TrajDiff generates object trajectories. These models leverage denoising diffusion probabilistic models (DDPM), which are known for their strong performance in generating high-quality, diverse outputs. The construction of these models involves training with a diffusion process that applies Gaussian noise in a forward manner, with the reverse process aiming to recover plausible motions.
2. Conditioning on Object Mass: The models can conditionally alter the generated motions based on a given mass value, allowing for adaptive changes in grasp and manipulation strategies—this includes altering fingertip and palm use based on the perceived weight of the object.
3. Surface Contact Frame: The implementation includes ConNet, which predicts hand-object contact positions conditioned on the mass, enhancing the plausibility of interactions by minimizing unrealistic effects such as floating artifacts.
4. User Input Handling: MACS can also adjust to user-specified trajectories by employing RatioNet, which adjusts trajectories to reflect realistic dynamics according to the object's weight, thus providing a seamless integration into artistic workflows or interactive applications.

Numerical Results and Claims

The paper provides insightful empirical evaluations demonstrating MACS's capability to generate qualitatively superior and realistic motion sequences compared to other generative baseline models like VAE and VAEGAN. Notably, diversity and multimodality metrics indicate that MACS produces motions with considerably higher variance and adaptability.

Physical Plausibility: Metrics such as non-collision and non-touching ratios, alongside collision distances, confirm the enhanced realism of the interactions synthesized by MACS. These measures are critical, as even minor unrealistic discrepancies can undermine the viewer's suspension of disbelief, notably in VR or gaming applications where immersion is key.

Generalizability: MACS exhibits generalization across unseen mass values, showcasing its robustness in extrapolating beyond the training dataset confines. Such adaptability is beneficial for applications that require nuanced interactions with variable object properties.

Implications and Future Directions

From a practical standpoint, the implications of this research are multifold, impacting fields such as virtual reality, robotics, and computer gaming. The capacity to generate realistic hand-object interactions with respect to mass can enhance user experience and provide more accurate training data for machine learning applications in robotics.

Theoretically, the approach could spur further exploration into integrating other physical properties, such as friction or surface texture, expanding beyond static and dynamic mass considerations. The methodology sets a precedent for future research to investigate additional conditioning factors that influence interaction, potentially leading to more sophisticated models that can accurately simulate intricate real-world phenomena.

Overall, this paper presents a substantive contribution to 3D motion synthesis, offering a robust framework that integrates physiological considerations into computational models. Future extensions could explore more complex objects or interaction scenarios, further bridging the gap between virtual simulations and real-world physics.