AutoDecoding Latent 3D Diffusion Models
Evangelos Ntavelis Aliaksandr Siarohin Kyle Olszewski Chaoyang Wang Luc Van Gool Sergey Tulyakov
We present a novel approach to the generation of static and articulated 3D
assets that has a 3D autodecoder at its core. The 3D autodecoder framework
embeds properties learned from the target dataset in the latent space, which
can then be decoded into a volumetric representation for rendering
view-consistent appearance and geometry. We then identify the appropriate
intermediate volumetric latent space, and introduce robust normalization and
de-normalization operations to learn a 3D diffusion from 2D images or monocular
videos of rigid or articulated objects. Our approach is flexible enough to use
either existing camera supervision or no camera information at all -- instead
efficiently learning it during training. Our evaluations demonstrate that our
generation results outperform state-of-the-art alternatives on various
benchmark datasets and metrics, including multi-view image datasets of
synthetic objects, real in-the-wild videos of moving people, and a large-scale,
real video dataset of static objects.