Overview

The workshop aims to explore the use of synthetic data in training and evaluating computer vision models, as well as in other related domains. During the last decade, advancements in computer vision were catalyzed by the release of painstakingly curated human-labeled datasets. Recently, people have increasingly resorted to synthetic data as an alternative to laborintensive human-labeled datasets for its scalability, customizability, and costeffectiveness. 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 ensuring the generalizability of models trained on synthetic data remain. We hope the workshop can provide a forum to discuss and encourage further exploration in these areas.

Invited Speakers

The speakers haven’t been finalized, stay tuned for updates!

Angela Dai
Technical University of Munich

Antonio Torralba
Massachusetts Institute of Technology

Bharath Hariharan
Cornell University

Bolei Zhou
University of California, Los Angeles

Jia Deng
Princeton University

Kiana Ehsani
Vercept

Schedule

Stay tuned for updates!

Poster Session

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Awards

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Sponsorship

If you are interested in sponsoring SynData4CV workshop @ CVPR 2025, please reach out to Jieyu Zhang (jieyuz2@cs.washington.edu).

Call for Papers

We invite papers on the use of synthetic data for training and evaluating computer vision models. We welcome submissions along two tracks:

Full papers: Up to 8 pages, not including references/appendix.
Short papers: Up to 4 pages, not including references/appendix.

Accepted papers will be allocated a poster presentation and displayed on the workshop website. In addition, we will offer a Best Long Paper award, Best Paper Runner-up award, and Best Short Paper with oral presentation.

Topics

Potential topics include, but are not limited to:

Effectiveness: What is the most effective way to generate and leverage synthetic data? How "realistic" does synthetic data need to be?
Efficiency and scalability: Can we make synthetic data generation more efficient and scalable without sacrificing 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: What ethical questions and risks are associated with synthetic data (e.g. bias amplification), and how can we address them?
Applications: In addition to existing attempts on leveraging synthetic data for training visual recognition and vision-language models, 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? How much real-world data do we need (in the long run)?

Submission Instructions

Submissions should be anonymized and formatted using the CVPR 2025 template and uploaded as a single PDF. Note that our workshop is non-archival.

Submission link: OpenReview Link

Important workshop dates

Deadline for submission: March 31th, 11:59 PM Pacific Time
Notification of acceptance: April 9th, 11:59 PM Pacific Time (Tentative)
Camera Ready submission deadline: April 24th, 11:59 PM Pacific Time (Tentative)
Workshop date: June 11th, 2025 (Full day) (Tentative)