GenAssets: Generating in-the-wild 3D Assets in Latent Space
High-quality 3D assets for traffic participants are critical for multi-sensor simulation, which is essential for the safe end-to-end development of autonomy. Building assets from in-the-wild data is key for diversity and realism, but existing neural-rendering based reconstruction methods are slow and generate assets that render well only from viewpoints close to the original observations, limiting their usefulness in simulation. Recent diffusion-based generative models build complete and diverse assets, but perform poorly on in-the-wild driving scenes, where observed actors are captured under sparse and limited fields of view, and are partially occluded. In this work, we propose a 3D late...
| Authors | Ze Yang, Jingkang Wang, Haowei Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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