Finally, we present more visual examples from the Ani-
mals, Portraits, and Daily Life datasets in Figure 9-11. The
proposed Instant3D consistently produces high-quality text-
to-3D results with favorable multi-view consistency. No-
tably, its performance on the challenging Daily Life dataset
(Figure 11) underscores its capability to handle intricate
real-world text prompts.
Computation Costs. To evaluate the training costs of dif-
ferent methods, we report the number of rendered view im-
ages per prompt on average (Views-PP) vs. CLIP retrieval
probability (CLIP-RP) on the Animals set. Views-PP is
computed as:

Views-PP =

Iterations × Batch Size
Number of Prompts

,

(11)

which essentially measures the computation cost as the
number of images rendered for each prompt during train-
ing. CLIP-RP is defined as the average probability of as-
signing the correct prompt to a rendered image among a

1https://github.com/threestudio-project/threestudio

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