  Text-to-3D generation has recently seen significant progress. To enhance its
practicality in real-world applications, it is crucial to generate multiple
independent objects with interactions, similar to layer-compositing in 2D image
editing. However, existing text-to-3D methods struggle with this task, as they
are designed to generate either non-independent objects or independent objects
lacking spatially plausible interactions. Addressing this, we propose
DreamDissector, a text-to-3D method capable of generating multiple independent
objects with interactions. DreamDissector accepts a multi-object text-to-3D
NeRF as input and produces independent textured meshes. To achieve this, we
introduce the Neural Category Field (NeCF) for disentangling the input NeRF.
Additionally, we present the Category Score Distillation Sampling (CSDS),
facilitated by a Deep Concept Mining (DCM) module, to tackle the concept gap
issue in diffusion models. By leveraging NeCF and CSDS, we can effectively
derive sub-NeRFs from the original scene. Further refinement enhances geometry
and texture. Our experimental results validate the effectiveness of
DreamDissector, providing users with novel means to control 3D synthesis at the
object level and potentially opening avenues for various creative applications
in the future.
