REVIEW 4 cited by
CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
With the onset of diffusion-based generative models and their ability to generate text-conditioned images, content generation has received a massive invigoration. Recently, these models have been shown to provide useful guidance for the generation of 3D graphics assets. However, existing work in text-conditioned 3D generation faces fundamental constraints: (i) inability to generate detailed, multi-object scenes, (ii) inability to textually control multi-object configurations, and (iii) physically realistic scene composition. In this work, we propose CG3D, a method for compositionally generating scalable 3D assets that resolves these constraints. We find that explicit Gaussian radiance fields, parameterized to allow for compositions of objects, possess the capability to enable semantically and physically consistent scenes. By utilizing a guidance framework built around this explicit representation, we show state of the art results, capable of even exceeding the guiding diffusion model in terms of object combinations and physics accuracy.
Forward citations
Cited by 4 Pith papers
-
BANG: Dividing 3D Assets via Generative Exploded Dynamics
A diffusion-based method that generates smooth exploded-view sequences of 3D objects, enabling part-level decomposition, control, and reassembly.
-
Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation
Hallo4D uses vision-language models to detect and correct spatial and temporal mistakes in AI-generated 3D and 4D content, improving consistency without retraining the base generators.
-
LL3M: Large Language 3D Modelers
A multi-agent LLM system generates editable 3D assets as Blender Python code, using documentation retrieval and visual self-critique to refine results.
-
DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation
A pipeline that generates editable 3D scenes from natural language by combining LLM-based layout planning, multi-timestep diffusion distillation, and staged camera sampling.
Discussion (0). Continue with ORCID to comment.