The challenge of weak supervision motivates us to devise
more effective condition mechanisms that can better inject
text information into the network, alleviating the difficulty
of learning the relationship between the two drastically dif-
ferent modalities: text and 3D. As the core innovation of
Instant3D, our proposed solution to this challenge involves
the fusion of three modules: cross-attention, style injection,
and token-to-plane transformation. The integration of these
components collectively ensures the precise alignment of
the generated 3D output with the condition text prompt.

Moreover, we also propose a simple yet effective acti-
vation function, called scaled-sigmoid, to replace the origi-
nal sigmoid function in NeRF, which speeds up the network
convergence by more than ten times.

Meanwhile, text-to-3D methods often suffer from se-
vere Janus (multi-head) problems. While the Perp-Neg al-
Instant3D