5. Conclusion

In this paper, we demonstrate the feasibility of fast text-
to-3D generation with a one-pass feedforward network. To
overcome the learning difficulties brought by weak super-
vision from SDS loss, we integrate three condition mech-
anisms, i.e., cross-attention, style injection, and token-to-
plane transformation, to better embed text prompts into a
decoder network. We also present a scaled-sigmoid func-
tion for albedo activation, which accelerates the optimiza-
tion convergence by more than ten times. In addition, we
propose an adaptive Perp-Neg algorithm, which effectively
tackles the Janus problem in our new paradigm without in-
troducing extra computation costs. Extensive experiments
on three prompt sets show that our Instant3D is able to gen-
erate faithful 3D objects for novel prompts in less than one
second. Moreover, our approach shows much better opti-
mization efficiency. We expect this work to inspire further
explorations in fast text-to-3D generation.

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Instant3D