A language-driven system generates semantically consistent multimodal textures from text prompts by linking autoregressive haptic models and diffusion-based visuals through a shared latent representation.
Preference-driven texture modeling through interactive generation and search,
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Active learning via human-in-the-loop optimization is used to tune fractional-order viscoelastic model parameters for high perceived realism in haptic rendering, with aggregation for population-level parameters.
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Language-Guided Multimodal Texture Authoring via Generative Models
A language-driven system generates semantically consistent multimodal textures from text prompts by linking autoregressive haptic models and diffusion-based visuals through a shared latent representation.
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Active Learning of Fractional-Order Viscoelastic Model Parameters for Realistic Haptic Rendering
Active learning via human-in-the-loop optimization is used to tune fractional-order viscoelastic model parameters for high perceived realism in haptic rendering, with aggregation for population-level parameters.