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Synthetic Data357 papers Synthetic Data233 papers Synthetic Data Augmentation155 papers Synthetic Dataset54 papers Synthetic Tabular Data42 papers Synthetic Training Data33 papers

541–570 of 2,587

Papers

ProcFunc: Function-Oriented Abstractions for Procedural 3D Generation in Python
Apr 29, 2026Alexander Raistrick, Karhan Kayan, Jack Nugent +8Procedural Abstraction
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
May 1, 2026Chenyu Huang, Jianghao Lin, Zhengyang Tang +4Monotonic Inventory Estimation Alphaevolve
AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos
Apr 21, 2026Jiagao Hu, Daiguo Zhou, Danzhen Fu +6Controllable Video Generation Autonomous Driving
The C-index illusion: discrimination without calibration in published survival models
Date pendingRafael da Silva, Danilo AlvaresSurvival Predictions Survival Analysis
Real2Sim2Real for Vision-Language-Action Manipulation: An AMD ROCm-Based Pipeline
Jul 25, 2026Qing Yang, Xun Wang, Ziguan Wang +3Vision-Language-Action Models Sim-To-Real Gap
Random-Effects Algorithm for Random Objects in Metric Spaces
May 4, 2026Marcos Matabuena, Mateo CámaraMetric Spaces Logistic Regression
BLPR: Robust License Plate Recognition under Viewpoint and Illumination Variations via Confidence-Driven VLM Fallback
Apr 10, 2026Guillermo Auza Banegas, Diego Calvimontes Vera, Natalia Condori Peredo +2License Plate Character Detection Illumination
mmPISA-bench: Do LLMs Reason Equally Well Across 43 Languages?
Jun 5, 2026Yerzhan Sapenov, Jaromir SavelkaMultilingual Reasoning Multilingual Benchmark
TRACE: Capability-Targeted Agentic Training
Apr 7, 2026Hangoo Kang, Tarun Suresh, Jon Saad-Falcon +1Agentic Reinforcement-Learning Framework Agentic Tool Use
The Anatomy and Boundary of Adaptation under Temporal Tabular Shift
Sep 14, 2026Tianyu Wang, Xi Vincent Wang, Lihui Wang +2Parameter-Efficient Adaptation Tabular Foundation Models
LiLi: Lie Theory Based 3D LiDAR Scan Alignment Degeneracy Detection
Sep 15, 2026Vsevolod Hulchuk, Jan Bayer, Jan FaiglLight Detection And Ranging 3D Object Detection
OmniGCD: Abstracting Generalized Category Discovery for Modality Agnosticism
Apr 16, 2026Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh +2Generalized Category Discovery Modality-Specific Encoders
Quantum Generative Diffusion Model for Real-World Time Series
Jun 25, 2026Jack Waller, Filippo Caruso, Dimitrios Makris +2Quantum Computing Synthetic Data
Learning a Particle Dynamics Model with Real-world Videos
May 22, 2026Chanho Kim, Suhas V. Sumukh, Li FuxinPhysical Dynamics Physics Simulation
DynSHAP: Towards Explainable Dynamic Survival Analysis
Sep 14, 2026Nastasya Anokhina, Jonas Jür\ss, Pietro LiòSurvival Analysis Explainability
TimeRLM: Recursive Language Models Enable Precise Anomaly Localization in Long-Context Time-Series
Aug 4, 2026Nicolas Zumarraga, Lorenzo Steno, Ning Wang +9Time-Series Anomaly Detection Anomalous Frame Detection
How to Train Your Long-Context Visual Document Model
Feb 16, 2026Austin VeselkaEfficient Long-Context Inference Large Vision Language Models
Gen2Balance: Generative Balancing for Long-Tailed Video Action Recognition
Jun 21, 2026Prajwal Gatti, Simon Jenni, Fabian Caba Heilbron +1Open-Vocabulary Action Recognition Video Dataset
TEDD: Robust Detection of Unstable Temporal Features
Jun 10, 2026Ricardo Ribeiro Pereira, Bruno Casal Laraña, Nádia Soares +1
Internalized Reasoning for Long-Context Visual Document Understanding
Mar 31, 2026Austin VeselkaVisual Document Retrieval Longbench
Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models
May 5, 2026JuneHyoung Kwon, JungMin Yun, YoungBin KimLarge Vision Language Models Unlearning Aims
Multimodal Shared Latent Representation of Narration, Microscope and iOCT Images for Phase Recognition in Vitreoretinal Surgery
Aug 31, 2026Onur Izmitlioglu, Shervin Dehghani, Tarek Ghannoum +2Optical Coherence Tomography Microscopy
Anomaly Detection in General Ledger Data: Results from a Hybrid Approach
Sep 16, 2026Jan Gronewald, Alexander Michael Rombach, Sebastian Stephan +1Anomaly Detection Anomaly
Data-Driven Covariate Selection for Nonparametric and Cycle-Agnostic Causal Effect Estimation
May 7, 2026Ana Leticia Garcez Vicente, Gijs van Seeventer, Saber SalehkaleybarStructural Causal Model Conditional Independence
Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics
Jul 10, 2026Okezzi Ukorigho, Opeoluwa OwoyeleCombustion Control Reaction
A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights
May 16, 2026Fabiola Ricci, Claudia Merger, Sebastian GoldtNeural Network Feature Learning
Class Angular Distortion Index for Dimensionality Reduction
May 1, 2026Kaviru Gunaratne, Stephen Kobourov, Jacob MillerDimensionality Reduction Distance
Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
Oct 1, 2025Jiayi Zhang, Simon Yu, Derek Chong +4Diversity Large Language
Coupled Training with Privileged Information and Unlabeled Data
May 22, 2026Jiahao Shi, Omar Hagrass, Jason M. KlusowskiUnlabeled Data Model Training
LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction
Sep 14, 2026Siyao Wang, Florian Guitton, Shuojie Fu +3Long-Horizon Forecasting Long-Horizon Agents

Apr 29, 2026 · cs.CVJ/K move · Enter open · S save

ProcFunc: Function-Oriented Abstractions for Procedural 3D Generation in Python

Alexander Raistrick, Karhan Kayan, Jack Nugent, David Yan+7

Princeton University, Department of Computer Science

We introduce ProcFunc, a library for Blender-based procedural 3D generation in Python. ProcFunc provides a library of easy-to-use Python functions, which streamline creating, combining, analyzing, and executing procedural generation code. ProcFunc makes it easy to create large-scale diverse training data, by combinatorial compositions of semantic components. VLMs can use ProcFunc to edit procedural material and geometry code and can create new procedural code with significantly fewer coding errors. Finally, as an example use case, we use ProcFunc to develop a new procedural generator of indoor rooms, which includes a collection of new compositional procedural materials. We demonstrate the detail, runtime efficiency, and diversity of this room generator, as well as its use for 3D synthetic data generation. Please visit https://github.com/princeton-vl/procfunc for source code.

Procedural Abstraction

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