  Recent advances have shown promise in merging neural radiance fields (NeRFs)
with pre-trained diffusion models for text-to-3D object generation. However,
one enduring challenge is their inadequate capability to accurately parse and
regenerate consistent multi-object environments. Specifically, these models
encounter difficulties in accurately representing quantity and style prompted
by multi-object texts, often resulting in a collapse of the rendering fidelity
that fails to match the semantic intricacies. Moreover, amalgamating these
elements into a coherent 3D scene is a substantial challenge, stemming from
generic distribution inherent in diffusion models. To tackle the issue of
'guidance collapse' and enhance consistency, we propose a novel framework,
dubbed CompoNeRF, by integrating an editable 3D scene layout with object
specific and scene-wide guidance mechanisms. It initiates by interpreting a
complex text into an editable 3D layout populated with multiple NeRFs, each
paired with a corresponding subtext prompt for precise object depiction. Next,
a tailored composition module seamlessly blends these NeRFs, promoting
consistency, while the dual-level text guidance reduces ambiguity and boosts
accuracy. Noticeably, the unique modularity of CompoNeRF permits NeRF
decomposition. This enables flexible scene editing and recomposition into new
scenes based on the edited layout or text prompts. Utilizing the open source
Stable Diffusion model, CompoNeRF not only generates scenes with high fidelity
but also paves the way for innovative multi-object composition using editable
3D layouts. Remarkably, our framework achieves up to a 54\% improvement in
performance, as measured by the multi-view CLIP score metric. Code is available
at https://github.com/hbai98/Componerf.
