SceneActBench: Can Agents Act on the 3D Scenes They See?
This benchmark fills a gap in evaluating VLM agents on complete 3D scene interaction, but it is an incremental contribution as it primarily compiles existing tasks into a unified loop.
SceneActBench introduces a benchmark to evaluate VLM agents on acting in multi-object 3D scenes across five tasks, finding that current models score between 38.6 and 50.2 overall and none performs consistently well.
Vision-language model (VLM) agents increasingly use tools to act on 3D scenes rather than only describe them. Existing 3D benchmarks score textual responses or single-object operations, leaving agent action on complete multi-object 3D scenes under evaluated. We present SceneActBench, a benchmark for visually conditioned action across five 3D tasks under a unified agent-environment loop. Given PNG images or sampled video frames and, where applicable, supplied 3D assets, an agent acts on a 3D environment. We evaluate each final output against hidden ground truth with task-specific geometric metrics. SceneActBench comprises five tasks built from 210 source instances, yielding 520 task cases including paired input conditions. Every task runs through one fixed agent loop to keep the comparison fair. Across eleven proprietary VLM configurations, Overall scores span 38.6-50.2, and none performs consistently well across tasks. We further analyse where and how failures manifest.