StanfordVL/OmniGibson
Oct 2024 – Feb 2026
13 Months active
Languages Used
PythonDVCDockerfileJSONShellYAMLJupyter NotebookTOML
Technical Skills
Asset PipelineScripting3D Asset Pipeline3D Graphics3D Transformations3ds Max
Cem Gökmen
PROFILE
Cem Gökmen
Cem Gokmen developed and maintained the OmniGibson repository, delivering a robust 3D asset and data pipeline that streamlined asset onboarding, rendering, and simulation workflows. He engineered automation for model and material processing, introduced V-Ray material conversion aligned with OmniPBR standards, and enhanced data validation and versioning to ensure reproducibility. Leveraging Python, PyTorch, and 3ds Max scripting, Cem improved CI/CD reliability by standardizing Conda-based environments and optimized onboarding with refined setup scripts. His work addressed cross-platform compatibility, reduced manual intervention, and enabled scalable analytics and QA. The depth of his contributions strengthened infrastructure, data integrity, and developer productivity.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
376Total
Bugs
66
Commits
376
Features
135
Lines of code
588,394
Activity Months13
Your Network
22 peopleWork History
February 2026
3 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for StanfordVL/OmniGibson focused on standardizing CI/CD environment management and simplifying workflow execution. Delivered a Conda-based environment strategy across all CI/CD pipelines, improved reliability, and reduced maintenance overhead by removing per-workflow environment activations. Enabled Conda activation at the GitHub Actions entrypoint to ensure consistent environment usage across jobs and repos, paving the way for reproducible builds and faster onboarding for new contributors.
3 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for StanfordVL/OmniGibson focused on standardizing CI/CD environment management and simplifying workflow execution. Delivered a Conda-based environment strategy across all CI/CD pipelines, improved reliability, and reduced maintenance overhead by removing per-workflow environment activations. Enabled Conda activation at the GitHub Actions entrypoint to ensure consistent environment usage across jobs and repos, paving the way for reproducible builds and faster onboarding for new contributors.
February 2026
October 2025
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Key deliverable: Git Ignore Enhancement for Generated App Data in StanfordVL/OmniGibson. Added the 'appdata/' directory to the repository's .gitignore to prevent generated Omniverse application data from being tracked, ensuring temporary or user-specific data does not clutter the repository. Change implemented via commit 322a466c087ab95932a58d82318ee52739e8dfda ('Ignore appdata directory'). This month produced no major bug fixes for this repository. Impact includes reduced repository noise, lower risk of committing large/generated data, and improved CI consistency and onboarding due to a cleaner history. Technologies/skills demonstrated: Git ignore patterns, version control hygiene, and disciplined change communication.
October 2025
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Key deliverable: Git Ignore Enhancement for Generated App Data in StanfordVL/OmniGibson. Added the 'appdata/' directory to the repository's .gitignore to prevent generated Omniverse application data from being tracked, ensuring temporary or user-specific data does not clutter the repository. Change implemented via commit 322a466c087ab95932a58d82318ee52739e8dfda ('Ignore appdata directory'). This month produced no major bug fixes for this repository. Impact includes reduced repository noise, lower risk of committing large/generated data, and improved CI consistency and onboarding due to a cleaner history. Technologies/skills demonstrated: Git ignore patterns, version control hygiene, and disciplined change communication.
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for StanfordVL/OmniGibson: Focused on improving the onboarding/setup experience by delivering OmniGibson Setup Script Enhancements. The changes streamline installation of OmniGibson, Isaac Sim, and datasets with clearer CUDA version handling, scoped variables, and improved UI messaging. This work lowers friction for new users and reduces support overhead while increasing install success rates. Two commits were made to implement these changes, laying groundwork for further automation and reliability.
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for StanfordVL/OmniGibson: Focused on improving the onboarding/setup experience by delivering OmniGibson Setup Script Enhancements. The changes streamline installation of OmniGibson, Isaac Sim, and datasets with clearer CUDA version handling, scoped variables, and improved UI messaging. This work lowers friction for new users and reduces support overhead while increasing install success rates. Two commits were made to implement these changes, laying groundwork for further automation and reliability.
September 2025
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 – OmniGibson (StanfordVL) monthly summary: Delivered enhancements and fixes for cross‑platform rendering fidelity and stability. Key features include a new Clear Coat Layer for V-Ray materials aligned with OmniPBR standards, enabling fine control of coat_effect with a coat_amount parameter and updated scattering to reflect the coat layer. Major bug fix targeted Windows compatibility for PyTorch Dynamo by replacing a hardcoded epsilon with a dynamic one derived from torch.finfo and adjusting the singular matrix threshold to prevent failures. These changes improve visual realism, asset consistency with OmniPBR, and cross‑platform reliability, reducing maintenance overhead.
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 – OmniGibson (StanfordVL) monthly summary: Delivered enhancements and fixes for cross‑platform rendering fidelity and stability. Key features include a new Clear Coat Layer for V-Ray materials aligned with OmniPBR standards, enabling fine control of coat_effect with a coat_amount parameter and updated scattering to reflect the coat layer. Major bug fix targeted Windows compatibility for PyTorch Dynamo by replacing a hardcoded epsilon with a dynamic one derived from torch.finfo and adjusting the singular matrix threshold to prevent failures. These changes improve visual realism, asset consistency with OmniPBR, and cross‑platform reliability, reducing maintenance overhead.
June 2025
13 Commits • 5 Features
Jun 1, 2025In June 2025, the OmniGibson data and asset pipeline matured through a sequence of automation, data-validation, and tooling improvements, delivering more reliable asset processing and cleaner substance/data pipelines. The work enabled faster asset onboarding, reduced manual cleanup, and improved consistency across materials, textures, and scene assets, while enhancing model/texture processing and V-Ray material conversion workflows.
13 Commits • 5 Features
Jun 1, 2025In June 2025, the OmniGibson data and asset pipeline matured through a sequence of automation, data-validation, and tooling improvements, delivering more reliable asset processing and cleaner substance/data pipelines. The work enabled faster asset onboarding, reduced manual cleanup, and improved consistency across materials, textures, and scene assets, while enhancing model/texture processing and V-Ray material conversion workflows.
June 2025
May 2025
60 Commits • 20 Features
May 1, 2025May 2025 highlights for StanfordVL/OmniGibson: Delivered key data and UI enhancements, strengthened reliability and data integrity, and advanced infrastructure and developer productivity. Core features include bounding box information, unified cocoa containers, UI updates, and comprehensive HSF complaint processing, paired with end-to-end task matching, a versioning/file-hash system, and auto-generated views. These changes increase data richness, reduce errors, and support scalable analytics and operations.
May 2025
60 Commits • 20 Features
May 1, 2025May 2025 highlights for StanfordVL/OmniGibson: Delivered key data and UI enhancements, strengthened reliability and data integrity, and advanced infrastructure and developer productivity. Core features include bounding box information, unified cocoa containers, UI updates, and comprehensive HSF complaint processing, paired with end-to-end task matching, a versioning/file-hash system, and auto-generated views. These changes increase data richness, reduce errors, and support scalable analytics and operations.
April 2025
79 Commits • 40 Features
Apr 1, 2025April 2025 saw OmniGibson deliver foundational tooling improvements, notebooks, documentation, and workflow enhancements to strengthen data pipelines, rendering reliability, and collaboration. The month emphasized business value through improved dataset handling, faster PRs via file manifest tooling, and richer scene tooling for faster iteration.
79 Commits • 40 Features
Apr 1, 2025April 2025 saw OmniGibson deliver foundational tooling improvements, notebooks, documentation, and workflow enhancements to strengthen data pipelines, rendering reliability, and collaboration. The month emphasized business value through improved dataset handling, faster PRs via file manifest tooling, and richer scene tooling for faster iteration.
April 2025
March 2025
19 Commits • 2 Features
Mar 1, 2025March 2025: Delivered robust QA data processing and knowledge base enhancements in OmniGibson, enabling multi-source complaint aggregation for QA 2025, stronger data quality, and an improved knowledge base UI. Key outcomes include end-to-end enhancements to the QA data pipeline, new complaint models and type tagging, and focused codebase maintenance that reduced technical debt. The work accelerated QA readiness, improved reporting accuracy, and strengthened data governance and platform maintainability.
March 2025
19 Commits • 2 Features
Mar 1, 2025March 2025: Delivered robust QA data processing and knowledge base enhancements in OmniGibson, enabling multi-source complaint aggregation for QA 2025, stronger data quality, and an improved knowledge base UI. Key outcomes include end-to-end enhancements to the QA data pipeline, new complaint models and type tagging, and focused codebase maintenance that reduced technical debt. The work accelerated QA readiness, improved reporting accuracy, and strengthened data governance and platform maintainability.
February 2025
29 Commits • 8 Features
Feb 1, 2025February 2025 – OmniGibson (StanfordVL). Delivered core model and tooling enhancements, improved stability across scripting, physics, and build artifacts, and advanced QA readiness to support faster, reliable releases. Focus areas included expanding the object model, refining view utilities, standardizing meta link handling, and enabling distribution artifacts. Implemented targeted fixes to JIT scripting, physics remeshing, and compatibility, while tightening QA workflows and category QA features to improve release quality and developer velocity.
29 Commits • 8 Features
Feb 1, 2025February 2025 – OmniGibson (StanfordVL). Delivered core model and tooling enhancements, improved stability across scripting, physics, and build artifacts, and advanced QA readiness to support faster, reliable releases. Focus areas included expanding the object model, refining view utilities, standardizing meta link handling, and enabling distribution artifacts. Implemented targeted fixes to JIT scripting, physics remeshing, and compatibility, while tightening QA workflows and category QA features to improve release quality and developer velocity.
February 2025
January 2025
110 Commits • 33 Features
Jan 1, 2025January 2025 (2025-01) — OmniGibson development progressed across UI, data-model, and batch stability, delivering a scalable, data-rich interface and high-quality data. Key features were implemented with a focus on business value, enabling faster data exploration and richer user experiences while maintaining data fidelity and performance.
January 2025
110 Commits • 33 Features
Jan 1, 2025January 2025 (2025-01) — OmniGibson development progressed across UI, data-model, and batch stability, delivering a scalable, data-rich interface and high-quality data. Key features were implemented with a focus on business value, enabling faster data exploration and richer user experiences while maintaining data fidelity and performance.
December 2024
36 Commits • 14 Features
Dec 1, 2024December 2024 monthly summary for StanfordVL/OmniGibson focused on delivering high-value features, hardening the importer pipeline, and improving build health. The team delivered measurable performance gains, expanded support for fillable meshes, and strengthened data handling and logging to enable faster iteration and reliable asset production.
36 Commits • 14 Features
Dec 1, 2024December 2024 monthly summary for StanfordVL/OmniGibson focused on delivering high-value features, hardening the importer pipeline, and improving build health. The team delivered measurable performance gains, expanded support for fillable meshes, and strengthened data handling and logging to enable faster iteration and reliable asset production.
December 2024
November 2024
21 Commits • 9 Features
Nov 1, 2024November 2024 Monthly Summary for StanfordVL/OmniGibson: Delivered focused improvements to CI/CD, production readiness, and validation pipelines, resulting in faster feedback, more stable deployments, and stronger data integrity. Key investments were in CI and Testing Automation, CUDA build pipeline enhancements, scale validation, caching for performance, and production-readiness hardening.
November 2024
21 Commits • 9 Features
Nov 1, 2024November 2024 Monthly Summary for StanfordVL/OmniGibson: Delivered focused improvements to CI/CD, production readiness, and validation pipelines, resulting in faster feedback, more stable deployments, and stronger data integrity. Key investments were in CI and Testing Automation, CUDA build pipeline enhancements, scale validation, caching for performance, and production-readiness hardening.
October 2024
1 Commits
Oct 1, 2024October 2024 monthly summary for StanfordVL/OmniGibson: focused on stabilizing exports by disabling global object corrections during scene export, aligning export results with source scenes, and reducing unintended modifications. The change targeted the export_scenes_global.py path to stop applying predefined object corrections during export.
1 Commits
Oct 1, 2024October 2024 monthly summary for StanfordVL/OmniGibson: focused on stabilizing exports by disabling global object corrections during scene export, aligning export results with source scenes, and reducing unintended modifications. The change targeted the export_scenes_global.py path to stop applying predefined object corrections during export.
October 2024
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Quality Metrics
Correctness87.8%
Maintainability88.2%
Architecture84.2%
Performance81.2%
AI Usage21.2%
Skills & Technologies
Programming Languages
BDDLC++CSSDVCDockerfileGitHTMLJSONJavaScriptJinja
Technical Skills
3D Asset Manipulation3D Asset Pipeline3D Environment Simulation3D Graphics3D Modeling3D Rendering3D Simulation3D Transformations3ds Max3ds Max Automation3ds Max Pipeline3ds Max Scripting3ds Max Scripting (pymxs)AIAI Development
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