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Visual agents for symbolic graphics reasoning depend on executable world representations, yet continued improvement remains bottlenecked by ambiguous supervision from final renders. Recent agentic systems refine scenes through repeated interaction, but they lack mechanisms to recursively extract actionable evidence fro...
This work presents Super-Gaussian, a novel VolVis framework that enhances scene editing and interaction in VR through intuitive 3D Gaussian selection and natural language interaction (NLI), and introduces a hierarchical select-and-refine workflow that combines random-walk-based region propagation, cluster selection, an...
CoBranchMR lets users branch an object into editable virtual copies, customize them independently, and then merge their work back into a shared object.
By enabling feature tracking directly on continuous representations, this method supports a new class of feature-driven visualization workflows centered on implicit models, and demonstrates the generality of this framework across a range of implicit representations, including analytic functions, MFAs, and INRs.
Experiments on multiple unseen scientific simulation datasets demonstrate that EVOLVE achieves substantially higher CRs than conventional compressors at comparable reconstruction quality, while delivering compression speeds that are orders of magnitude faster than INR-based methods, highlighting its promise as a strong...
This work systematically investigates Gaussian initialization strategies and proposes a simple yet effective scheme tailored for VolVis scenes, improving reconstruction accuracy and accelerating convergence, and evaluates ECoNGS qualitatively and quantitatively across various univariate and multivariate VolVis scenes.
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