ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging

• URL: http://arxiv.org/abs/2407.14100v1
• Date: Fri, 19 Jul 2024 08:12:41 GMT
• Title: ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging
• Authors: Guan Li, Yang Liu, Guihua Shan, Shiyu Cheng, Weiqun Cao, Junpeng Wang, Ko-Chih Wang,
• Abstract summary: ParamsDrag is a model that facilitates parameter space exploration through direct interaction with visualizations. First, the generative component of ParamsDrag generates visualizations based on the input simulation parameters. Second, by directly dragging structure-related features in the visualizations, users can intuitively understand the controlling effect of different parameters. Third, with the understanding from the earlier step, users can steer ParamsDrag to produce dynamic visual outcomes.
• Score: 10.860159623360842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Numerical simulation serves as a cornerstone in scientific modeling, yet the process of fine-tuning simulation parameters poses significant challenges. Conventionally, parameter adjustment relies on extensive numerical simulations, data analysis, and expert insights, resulting in substantial computational costs and low efficiency. The emergence of deep learning in recent years has provided promising avenues for more efficient exploration of parameter spaces. However, existing approaches often lack intuitive methods for precise parameter adjustment and optimization. To tackle these challenges, we introduce ParamsDrag, a model that facilitates parameter space exploration through direct interaction with visualizations. Inspired by DragGAN, our ParamsDrag model operates in three steps. First, the generative component of ParamsDrag generates visualizations based on the input simulation parameters. Second, by directly dragging structure-related features in the visualizations, users can intuitively understand the controlling effect of different parameters. Third, with the understanding from the earlier step, users can steer ParamsDrag to produce dynamic visual outcomes. Through experiments conducted on real-world simulations and comparisons with state-of-the-art deep learning-based approaches, we demonstrate the efficacy of our solution.

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