IT3D
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Improves Text-to-3D generation by leveraging explicitly synthesized multi-view images in the training loop.
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.
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