A. Setup
Baseline Methods. To evaluate the performance of our method
on text-driven 3D scene generation, we compare our method
with seven baseline methods, as shown in Table. I, including
four generation methods guided by the high-level seman-
tic prior (i.e., CLIP-Mesh [7], SJC [3], DreamFusion [2],
and DreamFusion-Scene) and three methods guided by the
low-level image prior (i.e., 3DP [30], PixelSynth [33], and
Text2Room [12]). Here, CLIP-Mesh, SJC, and DreamFusion
are three existing state-of-the-art text-to-3D methods which

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Fig. 5. Qualitative comparison of results generated by baselines and ours on different text prompts. Here, we only show two rendering results from different
views for each generated scene of each method due to space limitations. Please refer to the supplementary material for video results.
Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields