SceneActBench: Can Agents Act on the 3D Scenes They See?
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 envi
Record details
Published: 23 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 27 July 2026
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ethics.ai (23 July 2026), “SceneActBench: Can Agents Act on the 3D Scenes They See?,” evidence record 13599, https://ethics.ai/record/13599 (originally published by HuggingFace Daily Papers).
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