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
Jia Deng
Princeton University
Nupur Kumari
Nupur Kumari
Carnegie Mellon University
Manling Li
Manling Li
Northwestern University
Andrew Owens
Andrew Owens
Cornell Tech
Antonio Torralba
Antonio Torralba
Massachusetts Institute of Technology

Schedule


Workshop date: June 2026 (Full day)
Location: TBD

09:00 - 09:10 Opening Remarks
09:10 - 09:50 Invited Talk 1 (30 min talk / 10 min Q&A)
09:50 - 10:30 Invited Talk 2 (30 min talk / 10 min Q&A)
10:30 - 10:50 Coffee Break
10:50 - 11:30 Invited Talk 3 (30 min talk / 10 min Q&A)
11:30 - 12:10 Invited Talk 4 (30 min talk / 10 min Q&A)
12:10 - 13:30 Lunch
13:30 - 14:30 Poster Session
14:30 - 15:10 Invited Talk 5 (30 min talk / 10 min Q&A)
15:10 - 15:50 Invited Talk 6 (30 min talk / 10 min Q&A)
15:50 - 16:10 Coffee Break
16:10 - 16:25 Oral Presentation 1 (10 min talk / 5 min Q&A)
16:25 - 16:40 Oral Presentation 2 (10 min talk / 5 min Q&A)
16:40 - 17:00 Closing Remarks

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
  • Maximum 8 pages (excluding references)
  • Submissions should be made through OpenReview (link TBD)
  • All submissions will be double-blind reviewed

Important Workshop Dates

  • Deadline for submission: TBD
  • Notification of acceptance: TBD
  • Camera Ready submission deadline: TBD
  • Workshop date: June 2026 (Full day)

Related Workshops

Organizers

Jieyu Zhang
Jieyu Zhang
University of Washington
Weikai Huang
Weikai Huang
University of Washington
Zixian Ma
Zixian Ma
University of Washington
Rundong Luo
Rundong Luo
Cornell University
Shobhita Sundaram
Shobhita Sundaram
Massachusetts Institute of Technology
Wei-Chiu Ma
Wei-Chiu Ma
Cornell University
Ranjay Krishna
Ranjay Krishna
University of Washington