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Research questionHow can we generate scalable, photorealistic labeled data for monocular 3D human mesh estimation?Monocular images provide ambiguous depth, making accurate 3D mesh annotation expensive and difficult to scale. Rendered synthetic data offers precise labels but often lacks the photorealism and diversity needed for effective training.
AI
Computer Vision
Diffusion Models
Image Generation
Machine Learning
Latest papersRecent research connected to this question, newest first.PoseDreamer: Scalable and Photorealistic Human Data Generation Pipeline with Diffusion ModelsThe source describes a diffusion-based generation pipeline using controllable image generation, preference optimization, hard-sample mining, and quality filtering. It reports over 500,000 samples, improved image-quality metrics over rendering-based datasets, and downstream estimation performance comparable to or better than models trained on real or traditional synthetic data.research paper · Sep 3, 2026
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