LEGO-Eval: Towards Fine-Grained Evaluation on Synthesizing 3D Embodied
  Environments with Tool Augmentation
Authors: name: Gyeom Hwangbo, name: Hyungjoo Chae, name: Minseok Kang, name: Hyeonjong Ju, name: Soohyun Oh, name: Jinyoung Yeo

Despite recent progress in using Large Language Models (LLMs) for
automatically generating 3D scenes, generated scenes often lack realistic
spatial layouts and object attributes found in real-world environments. As this
problem stems from insufficiently detailed, coarse-grained instructions,
advancing 3D scene synthesis guided by more detailed, fine-grained instructions
that reflect real-world environments becomes crucial. Without such realistic
scenes, training embodied agents in unrealistic environments can lead them to
learn priors that diverge significantly from real-world physics and semantics,
degrading their performance when deployed. Thus, verifying the alignment
between the fine-grained instruction and the generated scene is essential for
effective learning. However, current evaluation methods, such as CLIPScore and
vision-language models (VLMs), often fail to reliably assess such alignment.
This shortcoming arises primarily from their shallow understanding of 3D
scenes, which often leads to improperly grounded scene components. To address
this, we introduce LEGO-Eval, an evaluation framework equipped with diverse
tools designed to explicitly ground scene components, enabling more accurate
alignment assessments. We also present LEGO-Bench, a benchmark of detailed
instructions that specify complex layouts and attributes of real-world
environments. Experiments demonstrate that LEGO-Eval outperforms VLM-as-a-judge
by 0.41 F1 score in assessing scene-instruction alignment. Benchmarking with
LEGO-Bench reveals significant limitations in current generation methods.
Across all evaluated approaches, success rates reached at most 10% in
generating scenes that fully align with fine-grained instructions.