Compress3D: a Compressed Latent Space for 3D Generation from a Single Image

arXiv:2403.13524v1 Announce Type: new
Abstract: 3D generation has witnessed significant advancements, yet efficiently producing high-quality 3D assets from a single image remains challenging. In this paper, we present a triplane autoencoder, which encodes 3D models into a compact triplane latent space to effectively compress both the 3D geometry and texture information. Within the autoencoder framework, we introduce a 3D-aware cross-attention mechanism, which utilizes low-resolution latent representations to query features from a high-resolution 3D feature volume, thereby enhancing the representation capacity …

3d models abstract arxiv autoencoder cs.ai cs.cv geometry image paper quality space type