NeoWorld-Pro: Programming Interactive Scenes from Monocular Images for Embodied Simulation

arXiv:2608.24212 · cs.CV · Submitted 2026-08-25 · Read on arXiv

Listen

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Hi, I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: We're looking at the paper: "NeoWorld-Pro: Programming Interactive Scenes from Monocular Images for Embodied Simulation".

Jane: Here's the gist of the paper. The gist NeoWorld-Pro proposes a framework that reformulates monocular scene reconstruction as procedural programming for interactive 3D environments, leveraging MLLMs to convert images into executable programs and employing a physics-in-the-loop mechanism to iteratively refine these programs for physical plausibility.

Tom: How it works NeoWorld-Pro moves beyond traditional dense reconstruction by shifting from unstructured geometric generation to a structured, program-centric paradigm where scenes are represented as "compositions of executable programs".

Jane: This approach translates a single RGB image into executable programs that explicitly define "object geometry via compositional primitives, articulation parameters, physical properties, and scene layout".

Tom: These programs are then compiled into standard URDF and USD assets.

Title and authors: Tom: So we’ve talked about what it does. The title itself, 'NeoWorld-Pro: Programming Interactive Scenes from Monocular Images for Embodied Simulation', tells you exactly the scope of this work. It’s not just about seeing a scene; it’s about programming an interactive one for simulation.

Jane: It highlights that key phrase, 'Programming Interactive Scenes'. That suggests the output isn't just a static model you look at, but something dynamic that can actually be manipulated in a virtual environment >

Tom: Exactly. And the authors are from Shanghai Jiao Tong University and Huazhong University of Science and Technology, which gives you a sense of the academic background behind this approach to scene construction >

Lu: The real implication here is reducing reliance on specific asset databases or supervised category-level priors. They’re using zero-shot reasoning and code synthesis from MLLMs instead of relying on pre-made libraries >

Meng: If it works as they claim, that means we don't need massive, hand-curated datasets for every single object type we want to simulate; the AI can generate the structure itself >

Lalam: The paper suggests that this new framework is a way to reduce reliance on specific asset databases and supervised category-level priors when building articulated assets from visual or geometric observations >

The paper's summary: Jane: To summarize, the paper introduces NeoWorld-Pro as a framework that takes an RGB image and turns it into executable programs. These programs specify things like object geometry, articulation, and physical properties >

Tom: Right. And the key mechanism they propose is this physics-in-the-loop refinement pipeline. It’s not just one step; it iteratively improves the generated programs by validating their execution in a physics engine >

Lu: They break this down into two main levels: an object-level loop that refines things like kinematic topology and joint limits, and a scene-level loop based on Cross-Entropy Method or CEM for optimizing scene plausibility >

Meng: So it’s not just generating the first guess; they are using simulation feedback to fix problems, like making sure joints don't fail or objects don't stack in a way that doesn't make sense physically >

Lalam: The paper constructs a benchmark with ninety articulated object categories and thirty scenes in USD format to validate how effective NeoWorld-Pro is on this task > <ref:2608.24212#pg3,90 articulated object categories and 30 scenes in>

The paper's improvements: Tom: They focus their validation on specific outcomes. For instance, they show that the framework optimizes physically plausible intra-object articulations under external forces, and stable inter-object spatial configurations under gravity >

Jane: It’s about ensuring simulation readiness by optimizing those things—things like free fall stability and how well objects can be assembled or manipulated in a task >

Lu: They use a hybrid neural representation, but they admit that it offers limited explicit control over simulation-critical geometry, articulation, and physical properties. This limitation pushes them to suggest something more explicit >

Meng: That's the part I worry about from a deployment standpoint. If the underlying representation lacks explicit control over those critical physical properties, it might be hard to deploy reliably at scale >

Lalam: The authors state that these limitations call for an explicit, executable world representation that supports structured editing and physics-based refinement >

Conclusion: Tom: So, looking at the whole picture of NeoWorld-Pro, it’s a system that uses MLLMs to generate code for scenes and then uses simulation feedback to polish those programs until they're physically plausible for manipulation tasks >

Jane: The results on their benchmark show that it significantly outperforms existing open-loop methods in generating environments that are immediately usable for robot learning, specifically achieving one hundred point zero zero for scene-level inter-object penetration rate and object stability under gravity > <ref:2608.24212#pg3>

Lu: The implication is that we can build complex scenes with a level of physical grounding that's currently missing from many image-to-URDF methods >

Meng: But they also flag that the evaluation is constrained because it’s limited to rigid and articulated objects under quasi-static physics, and the evaluation only covers fewer than ten objects > <ref:2608.24212#pg3>

Lalam: That's a clear limitation: currently, this method is restricted to those specific physical conditions, and future work aims to extend it to support deformable assets and richer material properties >

Shanghai Jiao Tong University · Huazhong University of Science and Technology

cs.CV

Submitted: 2026-08-25

Updated: 2026-10-20

Comments: Accepted by NeurIPS 2026. Project page: https://neoworldproject.github.io/neoworld-pro-website/

Project page: https://neoworldproject.github.io/neoworld-pro-website

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 89/100

The gist: The gist NeoWorld-Pro proposes a framework that reformulates monocular scene reconstruction as procedural programming for interactive 3D environments, leveraging MLLMs to convert images into

Key concepts

Program-Centric Paradigm: Instead of creating unstructured 3D meshes from images, this approach treats scenes as compositions of executable code. Each program explicitly defines object geometry, articulation parameters, and physical properties. This structure makes the scene directly usable for physical simulation and allows for easy editing and verification by AI models.
Physics-in-the-Loop Refinement: This is a two-stage process to ensure physical realism. First, individual objects are refined by simulating them in physics environments like Isaac Sim to adjust parameters such as joint limits and collision geometry. Second, the entire scene is optimized using Cross-Entropy Method (CEM) guided by simulation feedback to improve overall stability and plausibility.
MLLM_Reasoner/Programmer: Large Multimodal Models are used as the core generative engine. The Reasoner predicts the scene hierarchy and semantic attributes, while the Programmer generates Blender geometry programs and URDF export scripts. This leverages MLLMs' rich commonsense knowledge to translate visual inputs into structured, executable code for 3D assets.

Summary

The gist NeoWorld-Pro proposes a framework that reformulates monocular scene reconstruction as procedural programming for interactive 3D environments, leveraging MLLMs to convert images into executable programs and employing a physics-in-the-loop mechanism to iteratively refine these programs for physical plausibility.

How it works

NeoWorld-Pro moves beyond traditional dense reconstruction by shifting from unstructured geometric generation to a structured, program-centric paradigm where scenes are represented as compositions of executable programs. This approach translates a single RGB image into executable programs that explicitly define object geometry via compositional primitives, articulation parameters, physical properties, and scene layout. These programs are then compiled into standard URDF and USD assets. This program-centric paradigm is advantageous because it is executable for direct physical simulation, interpretable for MLLM-based inspection and verification, and editable for iterative scene refinement.

Object-Level Refinement

The framework employs a two-level physics-in-the-loop mechanism to ensure physical validity. The object construction process follows three stages:

Scene parsing, where an MLLM Reasoner predicts the scene hierarchy tree, 2D and 3D bounding boxes, and semantic attributes indicating whether an object serves as environmental support or is partially occluded.
Procedural asset programming, where an MLLM Programmer generates a Blender geometry program, an articulation program, and a URDF export script.
Object-level physics-in-the-loop refinement, which involves simulating the generated URDFs in Isaac Sim under two standardized physics scenarios: free-fall and force perturbation. This feedback is used to perform localized edits restricted to inertial parameters, joint limits, damping coefficients, object origins, and collision geometries.

Scene-Level Composition and Refinement

After individual objects are refined for physical consistency, the framework assembles them into a complete USD stage S. This assembly involves two stages:

Scene composition, which is deterministic scene composition from the inferred layout where coordinates are initialized based on a top-down traversal to ensure geometrically consistent initialization while reducing floating and interobject penetration.
CEM-based scene refinement, which uses the Cross-Entropy Method (CEM) for derivative-free optimization under simulation feedback. The refinement is guided by a hybrid reward function R(z) = wsemSsem(z; I) − wpenDpen(z) − wdriftDdrift(z), where Dpen measures SDF penetration across object pairs, while Ddrift measures pose drift during the free-fall rollout to encourage stacking stability.

Evaluation and Results

The framework is validated on a synthetic benchmark comprising 90 articulated object categories and 30 scenes in Universal Scene Description (USD) format. The results show that NeoWorld-Pro significantly outperforms existing open-loop methods in generating environments that are immediately usable for robot learning. Specifically, Table 3 shows that NeoWorld-Pro achieves 100.00 for both scene-level inter-object penetration rate (S-Pen↓) and scene/object stability under gravity (O-Stab↑)".

Limitations

NeoWorld-Pro is currently limited to rigid and articulated objects under quasi-static physics. The evaluation is also constrained by the fact that Our evaluation is also limited to scenes with fewer than 10 objects. Future work aims to extend the framework to support deformable assets, richer material properties, and denser scenes.

The paper is part of the NeoWorld Project. The preprint is available at arXiv:2608.24212v1 [cs.CV] 5,arXiv:2608.24213v1 [cs.

Improvements for AI systems

Incorporate physics-in-the-loop validation to ensure simulation readiness by iteratively refining generated programs based on dynamic feedback, such as collision events, instability, and articulation failures. This allows the AI system to produce environments that are truly 'simulation-ready' for high-precision downstream tasks.
Develop a program-centric paradigm where scenes are represented as executable code rather than unstructured meshes, enabling them to be executable for direct physical simulation, interpretable for MLLM-based inspection and verification, and editable. This structure allows the system to logically infer occluded internal structures and missing physical attributes without relying on dataset-specific category priors.
Implement a two-level refinement mechanism: an object-level loop that refines kinematic topology, joint limits, and inertial properties through MLLM-guided code editing, and a scene-level loop based on CEM that optimizes scene plausibility, ensuring both intra-object and inter-object consistency.
Enhance generalization by replacing reliance on curated libraries with zero-shot code-driven procedural modeling, which allows the framework to generate assets beyond category-specific asset distributions by exploiting the rich commonsense priors of MLLMs rather than dataset-specific training.
Improve downstream task success rates by using the feedback signal from manipulation tasks, such as a test-tube placement task, to iteratively refine the scene layout until the task succeeds, demonstrating that the reconstructed scene supports physically grounded downstream manipulation rather than merely visual resemblance.