VIST3A: Text-to-3D by Stitching a Multi-view Reconstruction Network to a
  Video Generator
Authors: name: Hyojun Go, name: Dominik Narnhofer, name: Goutam Bhat, name: Prune Truong, name: Federico Tombari, name: Konrad Schindler

The rapid progress of large, pretrained models for both visual content
generation and 3D reconstruction opens up new possibilities for text-to-3D
generation. Intuitively, one could obtain a formidable 3D scene generator if
one were able to combine the power of a modern latent text-to-video model as
"generator" with the geometric abilities of a recent (feedforward) 3D
reconstruction system as "decoder". We introduce VIST3A, a general framework
that does just that, addressing two main challenges. First, the two components
must be joined in a way that preserves the rich knowledge encoded in their
weights. We revisit model stitching, i.e., we identify the layer in the 3D
decoder that best matches the latent representation produced by the
text-to-video generator and stitch the two parts together. That operation
requires only a small dataset and no labels. Second, the text-to-video
generator must be aligned with the stitched 3D decoder, to ensure that the
generated latents are decodable into consistent, perceptually convincing 3D
scene geometry. To that end, we adapt direct reward finetuning, a popular
technique for human preference alignment. We evaluate the proposed VIST3A
approach with different video generators and 3D reconstruction models. All
tested pairings markedly improve over prior text-to-3D models that output
Gaussian splats. Moreover, by choosing a suitable 3D base model, VIST3A also
enables high-quality text-to-pointmap generation.