  Multi-view diffusion models, obtained by applying Supervised Finetuning (SFT)
to text-to-image diffusion models, have driven recent breakthroughs in
text-to-3D research. However, due to the limited size and quality of existing
3D datasets, they still suffer from multi-view inconsistencies and Neural
Radiance Field (NeRF) reconstruction artifacts. We argue that multi-view
diffusion models can benefit from further Reinforcement Learning Finetuning
(RLFT), which allows models to learn from the data generated by themselves and
improve beyond their dataset limitations during SFT. To this end, we introduce
Carve3D, an improved RLFT algorithm coupled with a novel Multi-view
Reconstruction Consistency (MRC) metric, to enhance the consistency of
multi-view diffusion models. To measure the MRC metric on a set of multi-view
images, we compare them with their corresponding NeRF renderings at the same
camera viewpoints. The resulting model, which we denote as Carve3DM,
demonstrates superior multi-view consistency and NeRF reconstruction quality
than existing models. Our results suggest that pairing SFT with Carve3D's RLFT
is essential for developing multi-view-consistent diffusion models, mirroring
the standard Large Language Model (LLM) alignment pipeline. Our code, training
and testing data, and video results are available at:
https://desaixie.github.io/carve-3d.
