VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images

• URL: http://arxiv.org/abs/2604.09531v1
• Date: Fri, 10 Apr 2026 17:48:51 GMT
• Title: VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images
• Abstract summary: Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition.<n>Natural image datasets provide limited supervision for low-level visual skills.<n>We introduce VisionFoundry, a task-aware synthetic data generation pipeline.
• Score: 27.57808835872072
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition. One plausible contributing factor is that natural image datasets provide limited supervision for low-level visual skills. This motivates a practical question: can targeted synthetic supervision, generated from only a task keyword such as Depth Order, address these weaknesses? To investigate this question, we introduce VisionFoundry, a task-aware synthetic data generation pipeline that takes only the task name as input and uses large language models (LLMs) to generate questions, answers, and text-to-image (T2I) prompts, then synthesizes images with T2I models and verifies consistency with a proprietary VLM, requiring no reference images or human annotation. Using VisionFoundry, we construct VisionFoundry-10K, a synthetic visual question answering (VQA) dataset containing 10k image-question-answer triples spanning 10 tasks. Models trained on VisionFoundry-10K achieve substantial improvements on visual perception benchmarks: +7% on MMVP and +10% on CV-Bench-3D, while preserving broader capabilities and showing favorable scaling behavior as data size increases. Our results suggest that limited task-targeted supervision is an important contributor to this bottleneck and that synthetic supervision is a promising path toward more systematic training for VLMs.

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