T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified
  Visual Modalities
Kangfu Mei Mo Zhou Vishal M. Patel
Diffusion Probabilistic Field (DPF) models the distribution of continuous
functions defined over metric spaces. While DPF shows great potential for
unifying data generation of various modalities including images, videos, and 3D
geometry, it does not scale to a higher data resolution. This can be attributed
to the ``scaling property'', where it is difficult for the model to capture
local structures through uniform sampling. To this end, we propose a new model
comprising of a view-wise sampling algorithm to focus on local structure
learning, and incorporating additional guidance, e.g., text description, to
complement the global geometry. The model can be scaled to generate
high-resolution data while unifying multiple modalities. Experimental results
on data generation in various modalities demonstrate the effectiveness of our
model, as well as its potential as a foundation framework for scalable
modality-unified visual content generation.