IT3D

Multi-view image supervision: Uses explicitly synthesized multi-view images as additional training signal for 3D generation, beyond standard per-view 2D supervision.

Diffusion-GAN dual training: Integrates a discriminator alongside the diffusion loss, producing a hybrid Diffusion-GAN training strategy for the 3D models.

Consistency gains: Improves geometric and photometric consistency across views compared to prior text-to-3D approaches.

Complements MVDream-style methods: Works well alongside multi-view diffusion priors, pointing toward increasingly sophisticated 2D-to-3D pipelines.