3D Hand-Object Motion Synthesis via Mass-Aware Diffusion Models
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Solution Overview
Problem
Existing animation technologies struggle to realistically depict interactions between humans and objects, particularly in regards to the physical properties such as mass, which affects the movement and handling of objects.
Innovation Solution
A system that determines the trajectory of an object based on its mass and generates a corresponding motion of a hand interacting with the object, using generative models like diffusion models to create realistic animations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional animation methods are used to depict human-object interactions, then the animation generation process is simple and fast, but the realism of the interaction is poor because physical properties like mass are not accurately reflected
Solution Approach 1:
The patent replaces traditional mechanical animation systems with a diffusion model-based generative system. Instead of using rigid kinematic constraints and physics engines, the system uses a neural network that learns physical interaction patterns from data, substituting mechanical computation with probabilistic generative modeling to achieve more realistic interactions
Solution Approach 2:
The system incorporates physical parameters such as mass directly into the diffusion model's conditioning inputs. By changing the model's input parameters to include object mass and interaction force characteristics, the generated animations automatically reflect physically accurate behaviors without requiring explicit physics calculations
2Reliability
If physical properties like mass are incorporated into animation generation, then the realism of object movement and hand motion is improved, but the computational complexity and processing requirements increase
Solution Approach 1:
The diffusion model is pre-trained on large datasets of physical interactions, performing the computationally intensive learning phase beforehand. During actual animation generation, the model efficiently samples from learned distributions, reducing real-time computational energy requirements while maintaining high physical accuracy
Solution Approach 2:
The system creates simplified representations or copies of physical interaction patterns through the diffusion model's latent space. Instead of performing full physics simulations, it generates approximate trajectories and motions that copy the essential characteristics of physical behavior at lower computational cost
Data Source
AI summary
A method includes determining a trajectory of an object based on a mass of the object, and determining a motion of a hand based on the mass of the object and the trajectory of the object. The method can further include generating an animation of the hand interacting with the object based on the trajectory of the object and the motion of the hand.