4DSynth is presented, a controllable procedural system that turns a natural-language description, a blueprint mask, or a single photograph into an editable 4D environment with explicit geometry, animated actors, collision-free trajectories, and physics-ready simulation state.
Abstract
Embodied agents need environments that are visually diverse, physically interactive, and changing over time. Procedural simulators can generate large interactive scene collections, and recent 4D generators produce compelling visual dynamics. Combining these properties in one environment, however, still demands extensive manual effort, and the result is rarely editable or controllable enough to reuse at scale. We present 4DSynth, a controllable procedural system that turns a natural-language description, a blueprint mask, or a single photograph into an editable 4D environment with explicit geometry, animated actors, collision-free trajectories, and physics-ready simulation state. Multiple scene routes share one geometry-grounded representation, so the same pipeline handles animation, camera planning, rendering, and task generation. To validate the full pipeline, we construct 4DSynth-Nav, an interactive navigation benchmark generated entirely from 4DSynth's procedural scenes. Two vision-language models evaluated across three difficulty tiers both fail the majority of tasks and stall after early subtasks. The same procedural controllability that produces these environments also makes each failure reproducible and each difficulty axis independently tunable. This paper presents both a controllable generation pipeline and the scalable benchmark it enables, offering a practical foundation for developing and evaluating embodied agents.
NeoWorld-Pro is proposed, a framework that reformulates monocular scene reconstruction as procedural programming for interactive 3D environments and outperforms open-loop and prior monocular reconstruction methods, while enabling complex downstream tasks such as stable stacking and fine-grained manipulation.
Yu-Meng He, Eachen Soong, Xiao-Tian Yang et al.· 1 citation
A framework that combines the merits of both parametric image-to-3D scene models and vision-language models, SceneMosaic is proposed, which obtains the initial candidate from the learned image-based prior, and subsequently evolves the result through VLM agents, ensuring both efficiency and physical validity.
RoomWright is presented, an agentic usage-driven framework for generating 3D scenes represented entirely as code for embodied interaction, providing interactive environments for embodied AI and policy learning.
Zijian Xiao, Zi-Peng Ye, Jin-Kun Hao et al.· 2 citations
Experiments show that Fysiverse-3D-SimReady improves input-view alignment, contact plausibility, and physical stability over existing single-image reconstruction and scene generation baselines, while enabling goal-conditioned physical interactions from a single image.
Lin-Tao Wang, Ming-Yang Sun, Yang Liu et al.· 0 citations
Text2Sim is presented, a simulation-specialized agentic pipeline that converts a text-only request into an executable, editable dynamic case and achieves higher scores than all four state-of-the-art baselines on both metrics.
Xiao-Yu Xiong, T. Wang, Yi-Ling Qiao et al.· 0 citations
Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on manual environment construction and calibration. We study whether frontier foundation models and coding agents can automate this process end to end. We formulate \emph{auto...
Jin-Zhou Tang, Z. Zhang, Jing Yang et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.