DataEvolver introduces a reusable framework with generation-time self-correction and validation-time self-expansion loops that improves visual datasets, shown to outperform baselines on an object-rotation task.
A scalable attention-based approach for image-to-3D texture mapping
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DataEvolver: Let Your Data Build and Improve Itself via Goal-Driven Loop Agents
DataEvolver introduces a reusable framework with generation-time self-correction and validation-time self-expansion loops that improves visual datasets, shown to outperform baselines on an object-rotation task.