Infinigen System: Procedural 3D Scene Generation

Updated 13 November 2025

Infinigen System is a procedural generation framework that synthesizes photorealistic 3D scenes with annotated assets using randomized mathematical models.
It features variants for natural scenes, indoor environments, stereo setups, and articulated simulations to support computer vision and robotics research.
The system employs constraint-based layout solvers and advanced rendering techniques to achieve superior zero-shot generalization and sample efficiency benchmarks.

The Infinigen System refers to a family of procedural generation frameworks distinguished by their use of randomized mathematical programs to create photorealistic synthetic data, spanning scene rendering (both natural and indoor environments), annotated asset synthesis, articulated object simulation, and downstream dataset optimization for machine learning applications. The core systems—Infinigen, Infinigen Indoors, Infinigen-Stereo, and Infinigen-Sim—deliver extensible procedural pipelines targeted at computer vision and robotics, enabling scalable generation of 3D scenes and assets with diverse geometry, materials, semantics, and annotations, and have established new performance baselines for zero-shot generalization, sample efficiency, and embodied agent training. All code and assets are open-sourced under the BSD license.

1. Procedural Engine Architecture and Variants

Infinigen was originally introduced as a procedural generator of photorealistic 3D scenes of the natural world (Raistrick et al., 2023). Every scene element—terrain, flora, fauna, weather, and materials—is synthesized de novo via parametric, randomized programs in Blender, using no external asset libraries. The engine modularizes scene creation into distinct stages:

Terrain/phenomena shape generation (signed distance fields, fractal/noise/cellular automata, L-systems),
Asset instantiation (procedural geometry, probabilistic assembly of parts),
Placement/instancing (rule-based, Poisson-disk, or semantic masks),
Rendering (Blender Cycles path tracing at 10k samples/pixel, with ground-truth annotation extraction: depth, normals, boundaries, segmentation, optical flow),
Output export (blend files, PNG/EXR images, JSON/NPZ metadata).

The later Infinigen Indoors engine adapts this paradigm to indoor environments (Raistrick et al., 17 Jun 2024), introducing a procedural asset library for furniture, appliances, architectural elements, and small objects (79 asset generators, ≈741 random variables). Scene construction is governed by a multi-stage constraint-based layout solver, allowing users to specify semantic, spatial, and geometric constraints via a Python-embedded DSL.

Infinigen-Stereo (Yan et al., 23 Apr 2025) layers stereo camera rig placement and additional scene randomization over the base pipeline, optimizing the procedural data distribution for zero-shot stereo matching network training.

Infinigen-Sim (Joshi et al., 15 May 2025) specializes in articulated simulation assets, providing Geometry Nodes+joint nodes for parametric object creation (e.g., doors, toasters, fridges), automatic kinematic blueprint emission, and export to URDF/USD/MJCF for robotics simulators.

2. Mathematical Formulations and Scene Randomization

All Infinigen systems are governed by random processes over high-dimensional parameter spaces. Each asset or scene attribute is defined as a function of random variables: $\mathcal{G}: \Theta_{\rm assets} \times \Theta_{\rm layout} \times \Theta_{\rm variation} \longrightarrow \mathcal{S}$ where input parameters include asset generator settings $\Theta_{\rm assets}$ , layout constraints $\Theta_{\rm layout}$ , and stochastic seeds $\Theta_{\rm variation}$ , yielding scene output $\mathcal{S}$ .

Continuous variables are sampled from uniform, Gaussian, log-uniform, or domain-specific distributions: $x \sim \mathcal{U}(a, b) \qquad l \sim \operatorname{LogUniform}(l_{\min}, l_{\max})$ Discrete choices operate via categorical distributions, e.g. material or asset class selection.

For articulated assets (Infinigen-Sim), the joint sampling is formalized as: $\theta \sim p(\theta) \quad \text{with joint kinematics:} \quad T^i_{\text{world}} = T^{p(i)}_{\text{world}} T^i_{\text{local}} \exp\bigl(\theta_i[\mathbf{a}_i]_\times\bigr)$ where $T^{p(i)}_{\text{world}}$ is the parent’s pose, $T^i_{\text{local}}$ the child’s local transformation, and $[\mathbf{a}_i]_\times$ the skew-symmetric matrix for joint axis $\mathbf{a}_i$ .

Layout constraint optimization in Infinigen Indoors is managed by soft-constraint loss minimization: $\ell(s) = \sum_{i} w_i f_i(s)$ employing multi-stage simulated annealing (addition, deletion, parameter resampling, translation, rotation) and strict rejection of hard constraints.

3. Constraint-Based Layout and Semantic Control

In indoor scene synthesis, spatial and semantic arrangement is specified by a compositional constraint language (DSL) with filtering (e.g., by object class), geometric primitives (e.g., minimum distances, alignments), and combinators (sum, mean, hinge loss). The constraint solver performs multi-stage simulated annealing over layout proposals, accepting moves per the Metropolis–Hastings criterion: $p(s'|s) = \min\left\{1, \exp\left(\frac{\ell(s) - \ell(s')}{\tau}\right)\right\}$ with temperature $\tau$ decaying exponentially.

Example constraints:

counts = scene.with_semantics('DiningChair').count()
tables = scene.with_semantics('DiningTable')
cost1 = hinge(counts - 6; lower=4, upper=8)
cost2 = angle_alignment(chairs, tables)
TotalScore = cost1 + cost2

Scene generation proceeds from floor-plan sampling (PCFG, Mondrian subdivision) through sequential object placement by semantic “size” tiers (large furniture, medium, small). The solver restricts object moves to subspaces determined by planar relationships (wall, floor, object).

4. Dataset Generation for Computer Vision and Robotics

The procedural pipelines output large-scale, perfectly annotated datasets for vision and robotics research. Exported ground truth from Infinigen or its derivatives includes RGB, metric depth, surface normals, occlusion boundaries, pixel-level segmentation, 3D bounding boxes, optical flow, and per-object semantics in standard formats (PNG/EXR/JSON/NPZ, URDF/USD/MJCF).

Infinigen-Stereo optimizes synthetic data distribution for zero-shot stereo matching through ablative studies:

Dense “floating-object” scenes (200 objects/frame) enhance geometric complexity, reducing Middlebury error from 12.52% to 6.60%.
Realistic backgrounds further boost performance; mixed indoor/nature/dense modes (33% each) yield best zero-shot accuracy (6.04%).
Material diversity, Bayesian augmentation of lighting, and wide stereo baseline sampling significantly improve sample efficiency and generalization.
Denoising after reduced-ray sample rendering recovers performance with 7.5× speed-up.
Final benchmarks: DLNR-Infinigen-150k achieves 3.76% 2 px error on Middlebury-H, surpassing baselines by >39%.

Infinigen-Sim outputs procedural articulated objects for manipulation and vision tasks:

Movable part segmentation: gains up to +5 AP for small moving parts when mixing Infinigen-Sim and PartNet training images (overall mAP rises from 48.23 to 50.13).
RL tasks (ManiSkill3): policies trained on combined Infinigen-Sim and PartNet sets show higher final success (e.g., 0.16 vs 0.12 at 2M steps on Toaster manipulation).
Sim-to-real policy transfer: 7/10 zero-shot successes on real robot (trained only on Infinigen-Sim), vs 0/10 (PartNet only).

5. Rendering and Export Infrastructure

All variants rely on Blender’s Cycles renderer (photorealistic path tracing, support for materials with subsurface scattering, volumetrics, blackbody emission). The rendering pipeline supports dynamic level-of-detail (LOD) adjustment (per-face area constraints), instancing of repeated assets, and per-pixel annotation via OpenGL-based mesh inspection (for depth and semantic output).

Export utilities generate simulation-ready assets in widely adopted formats:

.blend Blender files
USD (Universal Scene Description) for NVIDIA Omniverse
glTF/FBX for Unreal Engine
URDF/MJCF for robotics engines (Isaac Gym, ManiSkill3, Robosuite)
Collision meshes generated with convex decomposition (CoACD) where necessary
Consistent treatment of joint/skeleton hierarchy for articulated objects

Codebase size is approximately 40k lines; all pipelines scale across CPU and GPU resources, with scene generation fully parallelizable.

6. Performance, Impact, and Evaluations

The Infinigen family demonstrates substantial gains in both synthetic-to-real transfer and sample efficiency due to the combinatorial diversity and realistic material/semantic distributions in its procedural scenes and objects.

Stereo matching: RAFT-Stereo trained on Infinigen data improves zero-shot error rates and outperforms established synthetic datasets (Sceneflow, IRS, TartanAir).
Scene layout realism: Infinigen Indoors preferred in 79.5% of MTurk pairwise comparisons over leading alternative frameworks (ProcTHOR, ATISS, SceneFormer).
Downstream robustness: Computer vision models (e.g., ShadowFormer, U-Net for occlusion boundaries) yield superior PSNR and mAP when trained on Infinigen-generated sets.
Robotics: Infinigen-Sim-trained policies achieve higher RL task success and improve sim-to-real door manipulation transfer.
Real-time performance: Procedural indoor scenes run at 50–60 FPS in Omniverse on consumer RTX GPUs.

7. Limitations and Prospects

While the procedural paradigm provides theoretically infinite variation, several current limitations constrain realism and expressivity:

The constraint language (DSL) for scene control lacks temporal and agent-centric predicates and does not yet support natural language input.
Infinigen asset fidelity is high at the category level but does not encompass manufacturer- or brand-specific geometry or appearance.
Physical material properties (friction, restitution) and richer articulated joint types (spherical, continuous) are not universally covered.
Simulated annealing solvers in Infinigen Indoors can be greedy; gradient-based or learning-informed proposals are proposed areas for future work.
Real-world annotated evaluation, richer agent pathways, and expanded integration with LLM-driven scene generation (cf. Holodeck) remain open directions.

In summary, the Infinigen System has established itself as a methodological foundation for large-scale, high-fidelity synthetic data generation in vision, robotics, and graphics, blending procedural randomness, domain-specific control languages, and scalable rendering/export infrastructures. Its extensibility, reproducible pipelines, and demonstrated impact on generalization and sim-to-real transfer represent a reference point within procedural generation research (Raistrick et al., 2023, Raistrick et al., 17 Jun 2024, Yan et al., 23 Apr 2025, Joshi et al., 15 May 2025).