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Abstract Physically based simulation is often combined with geometric mesh animation to add realistic soft‐body dynamics to virtual characters. This is commonly done using constraint‐based simulation whereby a soft‐tissue simulation is constrained to geometric animation of a subpart (or otherwise proxy representation) of the character. We observe that standard constraint‐based simulation suffers from an important flaw that limits the expressiveness of soft‐body dynamics. Namely, under correct physics, the frequency and amplitude of soft‐tissue dynamics arising from constraints (“inertial amplitude”) are coupled, and cannot be adjusted independently merely by adjusting the material properties of the model. This means that the space of physically based simulations is inherently limited and cannot capture all effects typically expected by computer animators. For example, animators need the ability to adjust the frequency, inertial amplitude, gravity sag and damping properties of the virtual character, independently from each other, as these are the primary visual characteristics of the soft‐tissue dynamics. We demonstrate that independence can be achieved by transforming the equations of motion into a non‐inertial reference coordinate frame, then scaling the resulting inertial forces, and then converting the equations of motion back to the inertial frame. Such scaling of inertia makes it possible for the animator to set the character's inertial amplitude independently from frequency. We also provide exact controls for the amount of character's gravity sag, and the damping properties. In our examples, we use linear blend skinning and pose‐space deformation for geometric mesh animation, and the Finite Element Method for soft‐body constrained simulation; but our idea of scaling inertial forces is general and applicable to other animation and simulation methods. We demonstrate our technique on several character examples.
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The Dynamics of Social Interaction Among Evolved Model Agents
Abstract We offer three advances to the perceptual crossing simulation studies, which are aimed at challenging methodological individualism in the analysis of social cognition. First, we evolve and systematically test agents in rigorous conditions, identifying a set of 26 “robust circuits” with consistently high and generalizing performance. Next, we transform the sensor from discrete to continuous, facilitating a bifurcation analysis of the dynamics that shows that nonequilibrium dynamics are key to the mutual maintenance of interaction. Finally, we examine agents’ performance with partners whose neural controllers are different from their own and with decoy objects of fixed frequency and amplitude. Nonclonal performance varies and is not predicted by genotypic distance. Frequency-amplitude values that fool the focal agent do not include the agent’s own values. Altogether, our findings accentuate the importance of dynamical and nonclonal analyses for simulated sociality, emphasize the role of dialogue between artificial and human studies, and highlight the contributions of simulation studies to understanding social interactions.
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- Award ID(s):
- 2345446
- PAR ID:
- 10529299
- Publisher / Repository:
- MIT Press
- Date Published:
- Journal Name:
- Artificial Life
- Volume:
- 30
- Issue:
- 2
- ISSN:
- 1064-5462
- Page Range / eLocation ID:
- 216 to 239
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1162/artl_a_00417
