Augury and Forerunner: Real‐Time Feedback Via Predictive Numerical Optimization and Input Prediction.
Saved in:
| Title: | Augury and Forerunner: Real‐Time Feedback Via Predictive Numerical Optimization and Input Prediction. |
|---|---|
| Authors: | Graus, J.1 (AUTHOR) jgraus@gmu.edu, Gingold, Y.2 (AUTHOR) ygingold@gmu.edu |
| Source: | Computer Graphics Forum. Sep2025, Vol. 44 Issue 6, p1-17. 17p. |
| Subjects: | Interactive computer systems, Constrained optimization, Operations research, Mathematical optimization, Trajectory optimization, Surrogate-based optimization |
| Abstract: | In many interactive systems, user input initializes and launches an iterative optimization procedure. The goal is to provide assistive feedback to some creation/editing process. Examples include constraint‐based GUI layout and complex snapping scenarios. Many geometric problems, such as fitting a shape to data, involve optimizations which may take seconds to complete (or even longer), yet require human guidance. In order to make these optimization routines practical in interactive sessions, simplifications or sacrifices must be made. Canonically, non‐convex optimization problems are solved iteratively by taking a series of steps towards a solution. By their nature, there are many locally optimal solutions; which solution is found is highly dependent on an initial guess. There is a fundamental conflict between optimization and interactivity. Interrupting and restarting the optimization every time the user, e.g. moves the mouse prevents any solution from being computed until the user ceases interaction. Continuing to run the optimization procedure computes a perpetually outdated solution. This presents a particular unsolved challenge with respect to direct manipulation. Every time the user, e.g. moves the mouse, the entire optimization must be re‐started with the new user input, since returning a stale result associated with the previous user state is undesirable. We propose predictive short‐circuiting to reduce this fundamental tension. Our approach memoizes paths in the optimization's configuration space and predicts the trajectory of future optimization in real time, leveraging common C1$C^1$ continuity assumptions. This enables direct manipulation of formerly sluggish interactions. We demonstrate our approach on geometric fitting tasks. Additionally, we evaluate complementary mouse motion prediction algorithms as a means to discard or skip optimization problems that are irrelevant to the user's intended initial configuration for a targeted optimization procedure. Predicting where the mouse cursor will be located at the end of an operation, such as dragging a model of an engine component into scanned point cloud data to perform geometric alignment, allows us to pre‐emptively begin solving the targeted problem before the user finishes their movement. We take advantage of the fact that the prediction indicates the approximate energy basin the optimization procedure will need to explore. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 188175298 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Augury and Forerunner: Real‐Time Feedback Via Predictive Numerical Optimization and Input Prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Graus%2C+J%2E%22">Graus, J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jgraus@gmu.edu</i><br /><searchLink fieldCode="AR" term="%22Gingold%2C+Y%2E%22">Gingold, Y.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> ygingold@gmu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Graphics+Forum%22">Computer Graphics Forum</searchLink>. Sep2025, Vol. 44 Issue 6, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Interactive+computer+systems%22">Interactive computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Constrained+optimization%22">Constrained optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Operations+research%22">Operations research</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Trajectory+optimization%22">Trajectory optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Surrogate-based+optimization%22">Surrogate-based optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In many interactive systems, user input initializes and launches an iterative optimization procedure. The goal is to provide assistive feedback to some creation/editing process. Examples include constraint‐based GUI layout and complex snapping scenarios. Many geometric problems, such as fitting a shape to data, involve optimizations which may take seconds to complete (or even longer), yet require human guidance. In order to make these optimization routines practical in interactive sessions, simplifications or sacrifices must be made. Canonically, non‐convex optimization problems are solved iteratively by taking a series of steps towards a solution. By their nature, there are many locally optimal solutions; which solution is found is highly dependent on an initial guess. There is a fundamental conflict between optimization and interactivity. Interrupting and restarting the optimization every time the user, e.g. moves the mouse prevents any solution from being computed until the user ceases interaction. Continuing to run the optimization procedure computes a perpetually outdated solution. This presents a particular unsolved challenge with respect to direct manipulation. Every time the user, e.g. moves the mouse, the entire optimization must be re‐started with the new user input, since returning a stale result associated with the previous user state is undesirable. We propose predictive short‐circuiting to reduce this fundamental tension. Our approach memoizes paths in the optimization's configuration space and predicts the trajectory of future optimization in real time, leveraging common C1$C^1$ continuity assumptions. This enables direct manipulation of formerly sluggish interactions. We demonstrate our approach on geometric fitting tasks. Additionally, we evaluate complementary mouse motion prediction algorithms as a means to discard or skip optimization problems that are irrelevant to the user's intended initial configuration for a targeted optimization procedure. Predicting where the mouse cursor will be located at the end of an operation, such as dragging a model of an engine component into scanned point cloud data to perform geometric alignment, allows us to pre‐emptively begin solving the targeted problem before the user finishes their movement. We take advantage of the fact that the prediction indicates the approximate energy basin the optimization procedure will need to explore. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=188175298 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/cgf.70091 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Interactive computer systems Type: general – SubjectFull: Constrained optimization Type: general – SubjectFull: Operations research Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Trajectory optimization Type: general – SubjectFull: Surrogate-based optimization Type: general Titles: – TitleFull: Augury and Forerunner: Real‐Time Feedback Via Predictive Numerical Optimization and Input Prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Graus, J. – PersonEntity: Name: NameFull: Gingold, Y. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 01677055 Numbering: – Type: volume Value: 44 – Type: issue Value: 6 Titles: – TitleFull: Computer Graphics Forum Type: main |
| ResultId | 1 |
