Overview

Welcome to The 3rd Workshop on Synthetic Data for Computer Vision (SynData4CV) at CVPR 2026! During the last decade, advances in computer vision have been catalyzed by the release of meticulously curated human-labeled datasets. Recently, people have increasingly resorted to synthetic data as an alternative to labor-intensive human-labeled datasets for its scalability, customizability, and cost-effectiveness. Synthetic data offers the potential to generate large volumes of diverse and high-quality vision data, tailored to specific scenarios and edge cases that are hard to capture in real-world data. However, challenges such as the domain gap between synthetic and real-world data, potential biases in synthetic generation, and the generalizability of models trained on synthetic data remain. This workshop aims to provide a forum for discussion and encouragement of further exploration in these areas. Topics of interest include, but are not limited to:

Effectiveness: What is the most effective way to generate and leverage synthetic data? Does synthetic data need to "look" realistic?
Efficiency and scalability: Can we make synthetic data generation more efficient and scalable without much sacrifice on the quality?
Benchmark and evaluation: What benchmark and evaluation methods are needed to assess the efficacy of synthetic data for computer vision?
Risks and ethical considerations: How can we mitigate the risks of generating and using synthetic data? How do we address relevant ethical questions, such as bias amplification in synthetic datasets?
Applications: What are other tasks in computer vision or other related fields (e.g., robotics, NLP) that could benefit from synthetic data?
Other open problems: How do we decide which type of data to use, synthetic or real-world data? What is the optimal way to combine both if both are available?

Invited Speakers

Jia Deng
Princeton University

Georgia Gkioxari
Caltech

Nupur Kumari
Carnegie Mellon University

Manling Li
Northwestern University

Andrew Owens
Cornell Tech

Schedule

Workshop date: June 4, 2026, Afternoon (Half day)
Location: Room 607

1:00 PM - 1:10 PM	Opening Remarks
1:10 PM - 1:45 PM	Invited Talk: Manling Li
1:45 PM - 2:20 PM	Invited Talk: Jia Deng
2:20 PM - 2:55 PM	Invited Talk: Georgia Gkioxari
2:55 PM - 3:10 PM	Break
3:10 PM - 3:45 PM	Invited Talk: Andrew Owens
3:45 PM - 4:20 PM	Invited Talk: Nupur Kumari
4:20 PM - 4:30 PM	Closing Remarks
4:30 PM - 5:30 PM	Poster Session

Accepted Papers (sorted by alphabet)

Addressing Data Scarcity in Depth-Based Human Action Recognition via Zero-Shot Depth Estimation. Rebeka Angyal, Pedro Hermosilla, Martin Kampel, Irene Ballester

AfriST-VQA: Benchmarking MLLMs for Scene-Text Visual Question Answering for African Languages. Henry Gagnier

Appreciate the View: A Task-Aware Evaluation Framework for Novel View Synthesis. Saar Stern, Ido Sobol, Or Litany

Assessing the Predictive Value of Physics-Grounded Synthetic Data for Computer Vision in Space Environments. Arianna Issitt, Emily Happy, Elijah Clark, Mackenzie J. Meni, Ryan T. White

Auto-Comp: Scalable Controlled Synthetic Benchmarks for VL Compositionality. Cristian Sbrolli, Toshihiko Yamasaki, Matteo Matteucci

Avatar4D: Synthesizing Domain-Specific 4D Humans for Real-World Pose Estimation. Jerrin Bright, Zhibo Wang, Dmytro Klepachevskyi, Yuhao Chen, Sirisha Rambhatla, David A. Clausi, John S. Zelek

Beyond Objects: Contextual Synthetic Data Generation for Fine-Grained Classification. William Yang, Xindi Wu, Zhiwei Deng, Esin Tureci, Olga Russakovsky

Beyond Photorealism: Counterfactual Synthetic Bundles for Invariant Sim-to-Real Vision. Murari Ambati

Beyond Raw Signals: Undecoded Generative Latents as Privileged Synthetic Data. Cristian Sbrolli, Nicolas Michel, Matteo Matteucci, Toshihiko Yamasaki

Completing Missing Modalities: Synthetic Data for RGB–Infrared–Thermal–Text Person Re-Identification. Muhammad Umair, Muhammad Hammad Musaddiq, Jun Zhou, Ahmad Muhammad

CryoDiff: Cryo-EM Synthesis via Biophysics and Cycle-Consistent Diffusion. Genpei Zhang, Yuntian Yang, Siqi Wu, Ningyan Zhang, Seonghui Min, Jie Wu, Christopher Braxton Owens, Minhao Wu, Wanyue Feng, Gus LW Hart, Runmin Jiang, Min Xu

Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation. Ali Abbasi, Shima Imani, Chenyang An, Gayathri Mahalingam, Harsh Shrivastava, Maurice Diesendruck, Hamed Pirsiavash, Pramod Sharma, Soheil Kolouri

Disentangled Anatomy-Disease Diffusion (DADD) for Controllable Ulcerative Colitis Progression Synthesis. Umut Dundar, Alptekin Temizel

Durian: Dual Reference Image-Guided Portrait Animation with Attribute Transfer. Hyunsoo Cha, Byungjun Kim, Hanbyul Joo

Evaluating the Trade-offs of MDL-to-UsdPreviewSurface Material Simplification in NVIDIA Isaac Sim: Visual Quality, Feature Preservation, and AI Task Performance. Zihou Zhu, Mei Haitao, Haolong Zheng, Zhou Zhang

Few-Shot Synthetic Data Generation with Diffusion Models for Downstream Vision Tasks. Daniil Dushenev, Nazariy Karpov, Daniil Zinovjev, Alexander Gorin, Konstantin Kulikov

Fréchet Inception Distance is Failing to Preserve Rank Consistency for Synthetic Out-of-Distribution Samples. Linghui Liu, Henrike Stephani, Janis Keuper

Generating Synthetic Illumination Variation with Co-Located Relighting. Yash Turkar, Karthik K Dantu

Grounding Synthetic Data Generation With Vision and Language Models. Ümit Mert Çağlar, Alptekin Temizel

How Far Can We Go With Synthetic Data for Audio-Visual Sound Source Localization?. Arda Senocak, Sooyoung Park, Tae-Hyun Oh, Joon Son Chung

Hybrid Rendering for Multimodal Autonomous Driving: Merging Neural and Physics-Based Simulation. Máté Tóth, Péter Kovács, Réka Bencses, Zoltan Bendefy, Zoltan Hortsin, Balázs Teréki, Tamas Matuszka

iARCS: Iterative Agentic RL for Controllable 3D Scene Generation. Saugat Adhikari, Ashok Prasad Neupane, Pramish Paudel, Ajad Chhatkuli, Danda Pani Paudel

Masked Language Prompting for Generative Data Augmentation in Few-shot Fashion Style Recognition. Yuki Hirakawa, Ryotaro Shimizu

Multi-Objective Photoreal Simulation (MOPS) Dataset for Computer Vision in Robot Manipulation. Maximilian Xiling Li, Paul Mattes, Nils Blank, Rudolf Lioutikov

Narrowing the Performance Gap in Synthetic VLM Pre-training via Multi-Generator Ensembles. Leonardo Brusini, Cristian Sbrolli, Eugenio Lomurno, Toshihiko Yamasaki, Matteo Matteucci

Object-Centric Data Synthesis for Category-level Object Detection. Vikhyat Agarwal, Jiayi Cora Guo, Declan Hoban, Sissi Yuxi Zhang, Nick Moran, Peter Cho, Srilakshmi Pattabiraman, Shantanu H. Joshi

One Category One Prompt: Dataset Distillation using Diffusion Models. Ali Abbasi, Ashkan Shahbazi, Hamed Pirsiavash, Soheil Kolouri

OrbitArch. I-Ting Tsai, Bharath Hariharan

Personalized Generative Models for Contextual Debiasing. Xinran Liang, Esin Tureci, Prachi Sinha, Ye Zhu, Vikram V. Ramaswamy, Olga Russakovsky

PLLM: Pseudo-Labeling Large Language Models for CAD Program Synthesis. Yuanbo Li, Dule Shu, Yan-Ying Chen, Matthew Klenk, Daniel Ritchie

Privacy-Aware Synthetic Video Benchmarking and Relational Evaluation for Worker-Under-Suspended-Load Detection. Anshu Singh, Alejandro Seif

ProductConsistency: Improving Product Identity Preservation in Instruction-Based Image Editing via SFT and RL. Mukund Khanna, Raj Singh Yadav, Kunal Singh

RareCrafter: Controllable Generative Augmentation for Rare Object Detection in Driving Scenes. Mohadeseh Ghafoori, Danielle Lee, Kurt Hammen, Collin Meese, Mark Nejad

Realiz3D: 3D Generation Made Photorealistic via Domain-Aware Learning. Ido Sobol, Kihyuk Sohn, Yoav Blum, Egor Zakharov, Max Bluvstein, Andrea Vedaldi, Or Litany

Representation-Conditioned Diffusion Models for Guided Training Data Generation. Nithesh Chandher Karthikeyan, Jonas Unger, Gabriel Eilertsen

Restereo: Unifying diffusion stereo video generation and restoration. Xingchang Huang, Ashish Kumar Singh, Florian Dubost, Cristina Nader Vasconcelos, Sakar Khattar, Liang Shi, Christian Theobalt, Cengiz Oztireli, Gurprit Singh

SAIL: Similarity-Aware Guidance and Inter-Caption Augmentation-based Learning for Weakly-Supervised Dense Video Captioning. Ye-Chan Kim, SeungJu Cha, Si-Woo Kim, MinJu Jeon, HyunGee Kim, Dong-Jin Kim

Scaling Up 3D Forest Vision with Synthetic LiDAR. Yihang She, Andrew Blake, David Coomes, Srinivasan Keshav

Sea-Mie: Physically-Based Synthetic Fog for Maritime Image Defogging via Curriculum Learning. Stelios Avlakiotis, Peter Ter Heerdt, Thomas De Kerf, Steve Vanlanduit

Sequential Dataset for Satellite Pose Estimation and a Frequency-Space Neural Operator for HIL-Free Generalization Benchmarking. Woojin Cho, Junghwan Park, Steve Andreas Immanuel, JunminPark, Seokhyun Chin, Jiayun Wang

Sim-to-Real Metrology: Calibrated Digital Twins for Fringe Projection Profilometry. Noble Austine, Vuppu Eshwar Sai, Vaishnavi Ravi, Madhu S. Nair, Gorthi Rama Krishna Sai Subrahmanyam

SJEPA: Joint Embedding Predictive Architecture for Synthetic-to-Real Alignment. Shentong Mo

Structure-Consistent Joint Image-Mask Synthesis for Data-Scarce Medical Image Segmentation. Ningyan Zhang, Weiyi Zhang, Mostofa Rafid Uddin, Xingjian Li, Min Xu

Structure-retained low-rank adapters for weather synthesis. Shunxin Wang, Alexandros Stergiou, Luuk Spreeuwers, Estefania Talavera, Nicola Strisciuglio

StyleText: A Large-Scale Dataset and Benchmark for Stylized Scene Text Inpainting. Aleksandr Simonyan, Nipun Jindal

Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions. Jiaxiang Jiang, Mahesh Subedar, Omesh Tickoo

Synthetic Designed Experiments for Diagnosing Vision Model Failures. Krisanu Sarkar

Theory of Space: Evaluating Active Spatial Belief Construction in Foundation Models with Synthetic 3D Environments. Pingyue Zhang, Zihan Huang, Yue Wang, Jieyu Zhang, Letian Xue, Zihan Wang, Qineng Wang, Keshigeyan Chandrasegaran, Ruohan Zhang, Yejin Choi, Ranjay Krishna, Jiajun Wu, Li Fei-Fei, Manling Li

Vanast: Virtual Try-On with Human Image Animation via Synthetic Triplet Supervision. Hyunsoo Cha, Wonjung Woo, Byungjun Kim, Hanbyul Joo

Video-Consistent Synthetic Skiing Trajectories. M'Saydez Campbell, Rémi Emonet, Damien Muselet, Christophe Ducottet

WaterGen: Decoupling Scene and Medium in Underwater Image Generation. Jiayi Wu, Tianfu Wang, Tianyi Xiong, Dehao Yuan, Xiaomin Lin, Md Jahidul Islam, Cornelia Fermuller, Christopher Metzler, Yiannis Aloimonos

When Does Synthetic Data Help? A Spectral Theory of Task-Relevant Domain Gap with Applications to Guided Generation and Bias Auditing. Kaustubh S. Bukkapatnam, Rayan Malik

Why Training with Synthetic Data Fails for OOD: Distribution Gap Amplifies Noise Misalignment in Diffusion Models. Ying Hua, Jessica Bader, Jae Myung Kim, Zeynep Akata

WireSeg-32K: A Physics-Grounded Synthetic Dataset for Wire Instance Segmentation. Zilin Dai, Lehong Wang, Yi Yang, Xiang Fei

Call for Papers

We invite submissions on topics related to synthetic data for computer vision, including but not limited to:

Novel methods for generating synthetic data
Techniques for bridging the domain gap between synthetic and real data
Benchmarks and evaluation metrics for synthetic data
Applications of synthetic data in various computer vision tasks
Ethical considerations and bias mitigation in synthetic data generation
Efficient and scalable synthetic data generation pipelines

Submission Guidelines:

Papers should be formatted according to the CVPR 2026 template
Short papers: 4 pages (excluding references); Long papers: 8 pages (excluding references)
Submissions should be made through OpenReview
All submissions will be double-blind reviewed
Accepted papers will NOT be included in CVPR proceedings, so there are no double submission concerns.