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About this paper
Biao Zhang, Peter Wonka
LAGEMv proposes a hierarchical autoencoder for 3D representation learning, significantly enhancing efficiency and scalability for large-scale datasets. This architecture, combined with a cascaded diffusion framework, enables controllable generation of 3D models with detailed control over various levels of geometric detail.
Key insights
- Hierarchical architectures, akin to those in 2D image processing (e.g., U-Net, NVAE), can be effectively adapted to unordered 3D vector set representations.
- Normalizing latent vectors in the bottleneck to zero mean and unit variance simplifies the autoencoder training and aligns the latent space for diffusion models without requiring explicit KL divergence.
- A cascaded diffusion framework built upon a hierarchical latent space enables controlled 3D generation, allowing manipulation of geometric details at different scales.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.