HuLiGen: Human LiDAR Generation from Parametric Body Models
LiDAR point clouds of humans are extremely expensive to collect and annotate, thus represent a scarce resource that hinders the development of human analysis using this modality. To alleviate this scarcity, prior work relies on simulated human LiDAR, but such samples do not fully reflect the geometry and sensing characteristics of real observations. In contrast, we introduce HuLiGen, a generative model that generates human LiDAR point clouds from a parametric body model, using a point transformer trained with a flow-matching objective. We show that our generated point clouds are closer to the real capture distribution. Using HuLiGen to generate synthetic data, we propose a synthetic-only ...
| Authors | Salma Galaaoui, Nermin Samet, David Picard |
|---|---|
| Primary category | cs.CV |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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