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InfiniDream: Open-Ended Photorealistic 3D Worlds Through Procedural Generation

Yi-Chuan Huang, Hao-Jen Chien, Zheng-Hui Huang, Yu-Lun Liu, Kaipeng Zhang

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Abstract

Procedural generation plays a key role in synthetic data creation and game development, offering greater controllability, editability, and diversity than static mesh representations. However, VLM-based code generation often produces relatively simple 3D assets, while sophisticated photorealistic procedural systems require extensive manual development. Existing VLM-guided procedural methods remain largely constrained by predefined factories and APIs, limiting their ability to create novel asset types. We introduce InfiniDream, a VLM-driven framework for open-ended procedural 3D world generation. First, we propose Symbolically Constrained Asset Generation, which leverages existing procedural algorithms to create novel, photorealistic assets while preserving editability and seed-based diversity. Second, we develop an end-to-end text-to-world pipeline that combines semantic scene planning with Deterministic Frustum-Guided Grid Placement to construct large-scale 3D worlds with precise object placement, procedural population, and animation. Extensive evaluations on 100 object and 20 scene generation tasks demonstrate improvements across four perceptual metrics. Diversity analysis, ablation studies, and human preference evaluations further validate the effectiveness of our framework. Project page: https://yichuanh.github.io/infinidream-page/

Project page: https://yichuanh.github.io/infinidream-page/

Code: https://github.com/yichuanH/InfiniDream

Correspondence: [email protected], [email protected], [email protected]

Comparisons, ours, and open-ended worlds generated by InfiniDream

Figure 1. Beyond Predefined Factories: Rich, Editable Procedural Worlds. InfiniDream extends an existing procedural generator to create novel assets and visually rich 3D scenes. We compare its results with existing VLM-based 3D scene generation methods.

Introduction

Procedural generation creates 3D assets and scenes through executable programs, offering editability, diversity, and scalability. These advantages have made it widely applicable to synthetic data generation, game development, and virtual world creation [70]. However, conventional procedural generation methods rely heavily on handcrafted rules and predefined asset generators, limiting the variety of generated content and their ability to create novel assets and scenes.

Recent advances in the code generation and tool-use capabilities of large language models (LLMs) and vision-language models (VLMs) have enabled the generation and editing of 3D assets and scenes through executable Blender code [28, 55, 73, 110, 24, 96]. However, while existing approaches can construct scenes by composing assets or directly synthesizing geometry through code, generating large-scale 3D scenes with consistently rich geometric details, complex materials, and photorealistic appearance remains challenging.

Infinigen [70] is built on over 40,000 lines of code, including carefully handcrafted procedural asset factories, to generate photorealistic, editable 3D scenes. However, its generative capabilities remain constrained by predefined asset categories and procedural rules.

Recent studies have explored using LLMs and VLMs to control sophisticated procedural generation systems through function orchestration, parameter adjustment, and the composition of existing generators [82, 119, 117]. While these approaches improve the controllability of procedural scene generation, methods that primarily operate within predefined functions and parameter spaces remain limited in their ability to create novel asset types beyond the capabilities of existing generators.

Another line of research explores large-scale 3D world generation using retrieved or generatively synthesized mesh assets [23, 92]. Although these methods support instance-level editing, their mesh-based assets lack the parameterized editability and seed-based diversity of procedural generators. Producing diverse object variations often requires direct mesh manipulation or regeneration.

Figure 2 highlights three limitations: VLM Harness produces less photorealistic scenes, Infinigen is restricted to predefined factories, and Code2Worlds [117], a recent VLM-guided Infinigen framework, is limited to function calls and parameter adjustment. These limitations motivate open-ended procedural generation beyond existing factory capabilities.

Comparison of procedural generation strategies for a flowering cactus desert

Figure 2. Motivation: Beyond predefined procedural factories. Given a flowering cactus desert prompt, VLM-based code generation produces simplified geometry, predefined factories offer limited variations, and parameter-based generation struggles to introduce new features. InfiniDream extends existing procedural generators to create novel assets within rich, photorealistic 3D worlds.

To address these challenges, we introduce InfiniDream, a VLM-driven framework for open-ended procedural 3D world generation, designed to achieve realism, open-endedness, editability, diversity, and scalability. By employing VLM reasoning to compose and extend sophisticated procedural programs, InfiniDream generates novel assets beyond predefined factories while leveraging existing procedural algorithms. Our framework enables the creation of photorealistic, editable, and diverse large-scale 3D worlds from natural language descriptions.

At the asset level, Figure 3 further motivates our Symbolically Constrained Editing (SCE) strategy, which combines the flexibility of VLM-based code generation with the rich procedural structures of existing factories.

Comparison of procedural asset generation strategies for colorful butterflies and alpine hares

Figure 3. Comparison of procedural asset generation strategies. From left to right: VLM-generated Blender code from scratch, predefined Infinigen factories, VLM Agent Harness with full Infinigen access, and our symbolically constrained editing.

Our main contributions are summarized as follows:

  1. Open-Ended Procedural Asset Generation. We propose Symbolically Constrained Editing (SCE) to generate novel, photorealistic procedural assets with rich geometric details beyond predefined factories, while preserving editability and seed-based diversity.

  1. Scalable Procedural World Generation. We introduce an end-to-end text-to-world framework combining semantic scene planning and deterministic frustum-guided grid placement to generate large-scale, editable 3D worlds.

  1. Comprehensive Experimental Validation. We conduct extensive object- and scene-level evaluations, diversity analysis, ablation studies, and human preference studies to validate the effectiveness of InfiniDream.

Related Work

Procedural 3D Asset Generation. Procedural modeling encodes 3D content as executable rules, from L-systems [50, 66] and shape grammars [97, 60] to learned procedural shape and CAD programs [77, 89, 36, 37, 38, 72, 98, 41]. Advances in code generation [4] and tool use [75, 109] now let language models synthesize editable 3D assets as Blender code [13, 55, 73, 96, 105, 9, 69], other shape programs [114, 39, 94, 49], or CAD scripts for mechanical parts [2, 93, 74, 57]. Multimodal reasoning, retrieval augmentation [43], and iterative visual feedback [2, 93, 94, 49] further improve reconstruction, editing, and visual fidelity [29, 110, 24, 96]. Procedural material graphs are likewise generated or inferred from images [78, 21, 27, 44, 45], whereas inverse procedural modeling fits predefined procedural generators to targets [85, 91, 81, 63, 118] and remains bounded by their categories. Still, reliably synthesizing complex procedural geometry with rich geometric details and photorealistic appearance remains challenging. Benchmarks for 3D code generation [19, 96, 13, 2, 20, 107, 52] assess executability, geometric fidelity, and the benefits of execution feedback [6] and agentic coding [106]. However, 3DCodeBench [19] draws its procedural modeling categories from predefined Infinigen factories [70, 71], leaving procedural asset generation beyond these categories underexplored.

3D World Generation. Procedural content generation [80, 25, 76] has long produced noise-based terrains [64, 61] and cities [62], and now yields photorealistic natural and indoor scenes [70, 71, 69], simulation-ready assets [40], and houses for embodied AI [11]. Language models have been leveraged for 3D scene planning, spatial reasoning, and executable scene construction [28, 82, 119, 117, 15, 108, 3, 17, 83, 1, 116, 86, 22], and one line couples them with existing procedural generators through function orchestration, parameter adjustment, and composition of predefined procedural assets [82, 119, 67, 117, 115, 53, 87, 54]. Related approaches [28, 110, 117] also explore hierarchical generation [17, 84, 95], iterative visual feedback [51], physics-aware dynamic simulation [56], and looping animation of static 3D scenes [103]. However, methods restricted to orchestrating predefined procedural generators remain confined to their asset categories and parameter spaces, while semantically consistent and geometrically precise object placement in complex scenes remains challenging. Another line of work builds large-scale worlds [23, 92] from procedural layouts, retrieved assets [10, 108, 1, 12], generative 3D models [65, 26, 113, 101], and object-level composition [95, 31, 59], expanding generatable content and enabling instance-level manipulation. However, these approaches and neural scene generators [35, 46, 16, 112, 111, 88, 30, 5, 8, 18, 120, 14, 7, 48, 102, 42, 99, 58] generally lack the reusable procedural structures and high-level parameters needed for procedural editing and seed-based geometric variation. Orthogonally, recent work decouples explicit world states from generative video rendering [32, 34, 47] and benchmarks whether video models render what a program specifies [33]; such systems still require an explicit world specification, which procedural world generation can supply. InfiniDream addresses these gaps: it enables open-ended procedural asset generation beyond predefined factories and integrates it into scalable, fully procedural 3D world generation, while preserving procedural editability and seed-based diversity.

Method

We introduce InfiniDream, a VLM-driven framework for open-ended procedural 3D world generation. Given a text prompt, our framework selects a suitable initial scene and employs a Director to produce a structured Scene Plan specifying asset requirements, semantic placement constraints, and motion. Our framework consists of two core components: Symbolically Constrained Asset Generation, which creates novel procedural assets through constrained editing, and Deterministic Frustum-Guided Grid Placement, which translates semantic constraints into precise 3D object placements. Finally, procedural asset pooling, terrain refinement, and animation enable diverse, large-scale world generation.

Symbolically Constrained Asset Generation

Since directly providing a VLM with the entire Infinigen codebase often leads to imprecise editing (Figure 3), we propose Symbolically Constrained Editing (SCE), which extracts key code structures and explicit editing constraints, transforming a large procedural codebase into a manageable editing space (Figure 4).

Procedural asset generation through SCE

Figure 4. Procedural asset generation through SCE. (A) Factory mapping and retrieval. (B) Structured editing plan. (C) Coarse asset construction. (D) Render-guided fine refinement. Existing procedural algorithms are reused under explicit editing constraints.

Factory Mapping and Retrieval

Given a text description and an optional reference image, the system retrieves the most relevant Infinigen factory and constructs a Factory Brief (Figure 4(A)). It contains a Code Structure Map identifying key functions and dependencies, an Editable Surface consisting of asset-specific modules dynamically identified through depth-limited dependency analysis, and Linked Infrastructure providing read-only access to shared low-level algorithms (e.g., node graphs) via symbolic links. Together, these components establish explicit code-level constraints that restrict editing while preserving the underlying procedural algorithms. Further implementation details are provided in Appendix § 13.

Structured Planning and Coarse Construction

The Planner produces a structured Editing Plan specifying the editing strategy (Strategy), target locations (Locate), required modifications (Edit), dependencies (Rely-on), and potential risks (Risks), as shown in Figure 4(B). The plan also accounts for seed-dependent logic to preserve controllable procedural variation.

We define three editing strategies: Tweak adjusts existing parameters and high-level logic; Create introduces new procedural functions; and Promote selectively extends editing permissions to protected modules when necessary. These strategies enable controlled expansion of the editing space.

The Coarse Constructor executes the Editing Plan under these constraints (Figure 4(C)), preserving the original factory entry points and function signatures to maintain compatibility with the population pipeline and random seed control. It reuses existing algorithms to construct a coarse procedural asset. SCE preserves seed-dependent randomness by adjusting sampling ranges rather than fixing parameters to match the reference image.

Fine Refinement

As shown in Figure 4(D), the Reviser iteratively compares rendered results with the target description and reference image, identifying discrepancies in geometry, materials, and visual details. It then performs targeted code modifications within the established editing boundaries. A Selector identifies the best candidate across refinement iterations.

Through constrained editing and iterative refinement, SCE generates novel procedural assets while preserving procedural editability and seed-controlled diversity.

Semantic Placement Reasoning

Given a text prompt and current scene information, the Director first formulates a natural-language plan and converts it into a structured Scene Plan specifying editing operations (add, replace, and delete), asset requirements, and semantic placement constraints (Figure 5(A–B)).

Overview of InfiniDream’s end-to-end world generation pipeline

Figure 5. Overview of InfiniDream’s end-to-end world generation pipeline. (A) Scene Picker collects the user instruction and current scene information. (B) Director generates a structured Scene Plan. (C) Asset Generator creates procedural assets. (D) Deterministic frustum-guided placement identifies valid object locations. (E) Population instantiates diverse assets. (F) Terrain refinement and rendering produce the final scene.

The Director selects among three placement modes: Hero for semantically significant objects, Scattering for large-area distributions, and Swarm for collective entities. Hero placement supports seven Anchoring types (rooted, upright, tilted, conform, afloat, airborne, and submerged). A Semantic Target and Surface Policy further specify target regions, permissible surface types, slope limits, and support conditions. Scattering and Swarm are controlled by density, coverage, and group size.

The Director also determines the instance count NN, distinct procedural prototypes KK, World Motion, and Local Animation. World Motion specifies object trajectories and swept envelopes for collision checking. VLM decisions are restricted to discrete semantic operations and constraints, leaving continuous geometric computations to deterministic placement.

Deterministic Frustum-Guided Grid Placement

To avoid repeated VLM-based coordinate prediction and costly render-based verification, we propose Deterministic Frustum-Guided Grid Placement, which translates the Director’s semantic constraints into candidate 3D positions (Figure 5(D)). Candidates are evaluated in a near-to-far order without additional VLM calls or iterative rendering.

Frustum-based Candidate Generation

Given the specified placement region, we discretize the camera’s image plane into a 2D grid and generate 3D candidate positions along the corresponding camera rays. For surface-supported objects, candidates are determined by ray intersections with scene geometry. For airborne and submerged objects, candidates are sampled at discrete depth intervals along each ray.

Constraint-based Filtering and Depth-aware Placement

Each candidate undergoes five deterministic checks: (1) Visibility, (2) Collision with motion swept envelopes, (3) Inter-instance Spacing, (4) Surface Policy, including surface type, slope limits, and full support, and (5) Projected-area Band, requiring amin⁡≤a(p)≤amax⁡a_{\min} \leq a(p) \leq a_{\max}.

Given depth-sorted candidates C={p1,…,pn}C = \{p_1,\ldots,p_n\}, where gj(p)∈{0,1}g_j(p) \in\{0,1\} indicates satisfaction of constraint jj, we select the nearest valid candidate:

i∗=min⁡{i∣⋀j=15gj(pi)=1},p∗=pi∗.(1)i^* = \min\left\{i \mid\bigwedge_{j=1}^{5} g_j(p_i)=1\right\}, \qquad p^* = p_{i^*}. \tag*{(1)}

Combined with projected-area bounds, near-to-far search enables implicit size-aware placement, favoring closer positions for smaller objects and farther positions for larger ones. For multiple instances, the initial search depth increases progressively, while previously placed objects are included in subsequent collision checks. If no valid position exists, hierarchical constraint relaxation is applied (Appendix § 14).

Scalable Population and Animation

We combine the procedural assets generated in § 3.1 with the validated placements from § 3.3 through semantically controlled asset pooling, terrain refinement, and procedural animation (Figure 5(E)).

Semantically Budgeted Asset Pooling. To reduce the repeated construction cost of complex procedural assets, we extend Infinigen’s asset pooling mechanism to independent object population. The Director determines the number of instances NN and distinct procedural prototypes KK based on scene requirements. For assets supporting seed-based variation, we generate KK prototypes using different factory seeds and create NN instances through linked duplication:

M={Mk=G(sk)}k=1K,Ii=Clone⁡(M((i−1) mod K)+1).(2)\mathcal{M} = \{M_k = G(s_k)\}_{k=1}^{K}, \qquad I_i = \operatorname{Clone}\left(M_{((i-1)\bmod K)+1}\right). \tag*{(2)}

Here, GG denotes the procedural generator, sks_k is the seed for the kk-th prototype, and IiI_i is the ii-th instance. This mechanism reduces expensive asset constructions from NN to KK while maintaining seed-based diversity across prototypes. Assets unsuitable for pooling are generated independently.

Population, Terrain Refinement, and Animation. Procedural assets are placed according to their Anchoring modes and integrated with Infinigen’s fine terrain pipeline. Animation combines World Motion and Local Animation, with shared motion specifications ensuring consistency between placement constraints and actual trajectories. The final scene is then refined and rendered (Figure 5(E–F)).

Experiments

We evaluate InfiniDream at both the object and scene levels, complemented by diversity experiments, ablation studies, and a user study.

Evaluation Protocol and Baselines

Object-level Evaluation. We compare InfiniDream against five baselines: VLM (one-shot), VLM (harness), VIGA [110], BlenderRAG [73], and Infinigen [70]. Following 3DCodeBench [19], we adopt its codebase for the two VLM baselines, evaluating single-turn code generation and iterative refinement with a native coding-agent harness. The remaining baselines represent visual-feedback refinement, retrieval-augmented generation, and original Infinigen factory outputs, respectively.

Scene-level Evaluation. We compare large-scale world generation against VLM (harness), VIGA [110], Infinigen [70], and Code2Worlds [117], representing agentic code generation, visual-feedback refinement, original procedural scene generation, and LLM-driven hierarchical procedural orchestration, respectively.

Implementation and Metrics. We use Claude Opus 4.8 for InfiniDream and all VLM-based baselines, with Blender 4.x for generation and rendering. We evaluate 100 object-level tasks across six categories and 20 scene-level tasks at resolutions of 1024×10241024 \times1024 and 960×540960 \times540, respectively.

We report CLIP [68], SigLIP2 [90], DINOv3 [79], and ImageReward [104]. At the object level, CLIP, SigLIP2, and ImageReward are averaged over four fixed views (45∘45^\circ, 135∘135^\circ, 225∘225^\circ, and 315∘315^\circ), while DINOv3 compares the front view against four held-out reference images, independently generated from the same text prompt, and reports the mean cosine similarity. We additionally report code executability. Scene-level metrics use each method’s primary camera view. For scene-level DINOv3, each method’s primary camera view is compared against a single per-prompt scene reference image, generated by Gemini 3 Pro Image from the same scene prompt as a photorealistic wide-angle photograph of the entire scene; the same reference is shared by all methods and is never provided to any method during generation. Further details are provided in the Appendix.

Diversity Evaluation. We compare InfiniDream with TRELLIS.2 [100] on 15 text prompts. For each prompt, InfiniDream generates five variations using different random seeds, while TRELLIS.2 uses five independently generated reference images. We measure semantic consistency using SigLIP2 and visual diversity using average pairwise DINOv2 feature distance.

Experimental Results

Qualitative Comparisons

Figure 6 and Figure 7 present qualitative comparisons of InfiniDream with existing methods at the object and scene levels, highlighting differences in geometric detail, photorealism, and scene composition.

Qualitative comparison of object-level generation

Figure 6. Qualitative comparison of object-level generation. Generated 3D assets under identical text prompts.

Qualitative comparisons across four diverse environments

Figure 7. From text to richly detailed 3D worlds. Qualitative comparisons across four diverse environments, highlighting scene richness, visual fidelity, and semantic alignment. All methods use identical text prompts.

Quantitative Comparisons

As shown in Table 1 and Table 2, InfiniDream achieves the highest scores across all four perceptual metrics at both the object and scene levels.

MethodCLIP↑\uparrowSigLIP2↑\uparrowDINOv3↑\uparrowIR↑\uparrowExec. (%)↑\uparrow
VLM (One-shot)27.210.1200.419-1.1785
VLM (Harness)29.200.1390.498-0.8299
VIGA27.200.1130.395-1.1990
BlenderRAG27.560.1270.406-1.1591
Infinigen22.840.0790.253-1.90–
InfiniDream30.070.1480.554-0.6298

Table 1. Object-level comparison. Results on 100 tasks across six categories.

MethodCLIP↑\uparrowSigLIP2↑\uparrowDINOv3↑\uparrowIR↑\uparrow
VLM (Harness)28.850.1260.321-0.894
VIGA27.140.1210.246-1.433
Infinigen25.210.0790.182-1.710
Code2Worlds22.760.0720.114-2.014
InfiniDream29.640.1430.361-0.877

Table 2. Scene-level comparison. Results on 20 scene generation tasks.

Human Preference Study

We conduct a human preference study with 30 participants across 10 object and 10 scene tasks. Participants compare results from competing methods based on semantic alignment and visual quality. Table 3 reports InfiniDream’s preference rates for each criterion and overall.

LevelAlign. (%)Quality (%)Overall (%)
Object81.373.777.5
Scene91.075.383.2

Table 3. Human preference study. Preference rates for InfiniDream.

Diversity Analysis

For each text prompt, InfiniDream generates five variations using different random seeds, while TRELLIS.2 uses five independently generated reference images. We evaluate semantic consistency using SigLIP2 and visual diversity using average pairwise DINOv2 feature distance. InfiniDream achieves greater visual diversity while maintaining comparable semantic alignment (Figure 8, Table 4).

High-quality and diverse procedural assets

Figure 8. High-quality and diverse procedural assets. InfiniDream generates five variations by changing random seeds, while TRELLIS.2 uses five independently generated reference images.

MethodSigLIP2↑\uparrowDINOv2 Dist.↑\uparrow
TRELLIS.20.13170.6084
InfiniDream0.12250.6704

Table 4. Diversity comparison. Five variations per prompt.

Ablation Studies

Object-level Ablation

We evaluate six ablation settings on 25 animal and plant tasks: (1) w/o Ref. Image, (2) Random Factory, (3) w/o Planner, (4) Agent (Infinigen), (5) w/o Refiner, and (6) 3 Rounds. The full pipeline allows up to six refinement rounds. For Agent (Infinigen), Claude Opus 4.8 is given the entire Infinigen codebase and Bash access to autonomously write and edit code, render and inspect results, and determine when to stop within six rounds, without our structured planning or editing constraints. Quantitative and qualitative results are shown in Table 5 and Figure 9.

ConfigurationCLIP↑\uparrowSigLIP2↑\uparrowDINOv3↑\uparrowIR↑\uparrow
w/o Ref. Image28.680.13380.363-0.978
Random Factory27.390.11570.296-1.520
w/o Planner28.720.12740.368-1.023
Agent (Infinigen)29.010.13430.387-0.968
w/o Refiner28.800.11920.334-1.420
3 Rounds29.330.13540.438-0.824
Full30.370.14170.494-0.580

Table 5. Quantitative object-level ablation. Evaluation of generation components and refinement strategies across 25 animal and plant tasks, measuring semantic alignment, visual similarity, and perceptual quality.

Qualitative object-level ablation comparison

Figure 9. Qualitative object-level ablation. Visual comparison of different generation strategies, illustrating the effects of reference images, factory selection, structured planning, and iterative refinement on asset geometry, fine-grained details, and visual appearance.

Scene-level Ablation

We evaluate four settings on 20 scenes: (1) w/o Grid Search, using direct 3D coordinate prediction; (2) w/o Anchoring, reverting to Infinigen’s grounding; (3) w/o Semantic Target, removing semantic surface constraints; and (4) Agent (Placement), allowing a VLM agent to autonomously place our generated 3D assets in the same selected coarse scene given the text prompt, without our placement pipeline.

We evaluate three placement metrics: Unreadable Rate, the percentage of objects outside the camera view or occupying less than 0.1% of the image area; Ground-contact Error, the absolute distance between an object’s lowest contact point and its supporting surface, capturing floating or penetration; and Contact Spread, the difference between the maximum and minimum terrain-relative gaps across sampled contact points, capturing uneven grounding and partial suspension (surface-supported objects only; see Appendix § 16). We report the 95th percentile (P95) for the latter two metrics. For Contact Spread, P95 is computed over per-object values pooled across all surface-supported requested-add objects in the 20 scenes. Results are shown in Table 6 and Figure 10.

ConfigurationUnread. (%)↓\downarrowGround P95 (m)↓\downarrowSpread P95 (m)↓\downarrow
w/o Grid Search27.40.150.25
w/o Anchoring40.30.480.74
w/o Semantic Target14.00.150.38
Agent (Placement)50.90.540.75
Full (Ours)7.80.150.24

Table 6. Quantitative scene-level placement ablation. Evaluation on 20 scenes measuring object readability, ground contact, and contact spread.

Qualitative scene-level placement ablation comparison

Figure 10. Qualitative scene-level placement ablation. Visual comparison of different placement strategies, illustrating the effects of semantic planning, grid-based placement, and anchoring constraints on object visibility, spatial arrangement, and scene composition.

Conclusion

We presented InfiniDream, a VLM-driven framework for open-ended procedural 3D world generation. By combining Symbolically Constrained Asset Generation with semantic planning and deterministic placement, InfiniDream creates novel, photorealistic, and editable procedural assets and composes them into diverse, large-scale 3D worlds. Extensive experiments demonstrate its effectiveness at both the object and scene levels.

Limitations and Future Work. Our current animation pipeline relies on a predefined set of motion types. Future work could explore VLM-generated animation code to support more complex and customized motions. We also aim to extend InfiniDream to indoor environments with more complex spatial relationships and object interactions.

Appendix

Additional Object-level Comparisons

We provide additional qualitative comparisons of object-level generation across diverse asset categories in Figure 11. All methods are evaluated using identical text prompts under the same experimental protocol described in § 4.1. These additional examples further illustrate differences in geometric complexity, material details, and visual fidelity among the compared methods.

Additional qualitative comparisons of object-level generation

Figure 11. Additional qualitative comparisons of object-level generation. More examples comparing InfiniDream with existing methods under identical text prompts.

Additional Scene-level Comparisons

We provide additional qualitative comparisons of scene-level generation across diverse environments in Figure 12. All methods are evaluated using identical text prompts under the same experimental protocol described in § 4.1. These additional results illustrate differences in scene composition, geometric detail, semantic alignment, and visual fidelity across the compared methods.

Additional qualitative comparisons of scene-level generation across diverse environments

Figure 12. Additional qualitative comparisons of scene-level generation. More examples of 3D world generation across diverse environments under identical text prompts.

Additional Qualitative Comparison with GPT-6 Astra

We provide an additional qualitative comparison with GPT-6 Astra in Figure 13. This comparison is included as supplementary evidence beyond the main benchmark suite. Importantly, InfiniDream still uses Claude Opus 4.8 as the underlying VLM, while GPT-6 Astra serves as an additional strong proprietary baseline. Under identical text prompts, the results highlight differences in visual fidelity, geometric detail, material richness, and overall semantic composition.

Additional qualitative comparison with GPT-6 Astra

Figure 13. Additional qualitative comparison with GPT-6 Astra. We compare InfiniDream, implemented with Claude Opus 4.8, against GPT-6 Astra on representative examples under identical text prompts. The results provide additional evidence of InfiniDream’s ability to generate visually rich and semantically coherent procedural assets and scenes.

Additional Diversity Results

We provide additional qualitative diversity comparisons between InfiniDream and TRELLIS.2 in Figure 14. For each text prompt, InfiniDream generates five procedural variations by changing random seeds, while TRELLIS.2 generates five assets from independently generated reference images. These examples further illustrate the diversity of InfiniDream’s procedural assets while preserving semantic consistency and visual quality.

Additional qualitative diversity comparisons with TRELLIS.2

Figure 14. Additional qualitative diversity comparisons with TRELLIS.2. Five variations are generated for each text prompt. InfiniDream achieves procedural diversity by varying random seeds, whereas TRELLIS.2 uses independently generated reference images.

Object-level Comparison with Code2Worlds

We further compare InfiniDream with Code2Worlds on the 29 out of 100 object-level tasks supported by Code2Worlds. Due to its limited factory coverage, we report this comparison separately from the main benchmark.

As shown in Figure 15 and Table 7, InfiniDream achieves higher scores across all four metrics. Code2Worlds can exploit the parameter space of existing factories, such as adjusting the color of a fish, but cannot introduce novel procedural features beyond predefined generators. In contrast, InfiniDream extends existing procedural code to generate new geometric structures and fine-grained details, enabling more flexible open-ended asset generation.

Object-level comparison showing Reference, Infinigen, Code2Worlds, and Ours examples

Figure 15. Object-level comparison with Code2Worlds.

MetricInfiniDreamCode2Worlds
CLIP ↑\uparrow30.2422.56
SigLIP2 ↑\uparrow0.1530.080
ImageReward ↑\uparrow-0.35-1.94
DINOv3 ↑\uparrow0.5830.281

Table 7. Quantitative comparison. Results on 29 supported tasks.

Man-Made Asset Generation

Beyond natural assets, InfiniDream can retrieve and extend factories for man-made objects. As shown in Figure 16, while VIGA performs well on geometric objects, InfiniDream achieves comparable structural quality with rich geometric and material details, demonstrating its versatility across both organic and man-made assets.

Qualitative comparison of man-made asset generation showing Reference, VLM (one-shot), VLM (harness), VIGA, BlenderRAG, Infinigen, and Ours examples

Figure 16. Qualitative comparison of man-made asset generation. InfiniDream produces structurally coherent man-made objects with rich geometric and material details.

Implementation Details of Symbolically Constrained Editing

Reference Synthesis and Factory Retrieval. We use Gemini 3 Pro Image to generate an isolated reference object against a white background. Claude Opus 4.8 retrieves the most relevant Infinigen factory, with deterministic lexical matching as a fallback.

Factory Brief Construction. Starting from the selected factory, we perform AST-based import graph traversal with a maximum depth of two. Asset-specific modules form the Editable Surface, while shared modules remain read-only Linked Infrastructure. Unrelated dependencies are pruned, and a VLM summarizes the remaining code into a cached Code Structure Map.

Constrained Code Editing. The Planner produces an Editing Plan specifying Strategy, Locate, Edits, Rely-on, and Risks. The Constructor can modify existing modules (Tweak), promote protected modules (Promote), or synthesize new procedural modules (Create). Editing is restricted to the working directory, preserving the original factory interface and seed-dependent randomness.

Iterative Refinement. Modified modules are integrated into a symbolic-link overlay of the original Infinigen codebase. A vision-based Reviser guides targeted refinement, while execution failures trigger traceback-based repair. The pipeline supports up to six rounds with early stopping. A vision-based Selector chooses the best successful candidate, whose code snapshot is used to reconstruct the final executable generator.

Implementation Details of Deterministic Frustum-Guided Grid Placement

Candidate Generation. We discretize the Director’s screen-space region into a 9×59 \times5 grid (17×917 \times9 for six or more instances). Surface candidates are obtained through ray-scene intersections, while airborne and submerged candidates are sampled along camera rays using geometric depth intervals (×1.3\times1.3, up to 80 m). Larger objects are placed first, with deterministic hashing ensuring reproducibility. The entire placement process requires no additional VLM calls.

Constraints and Relaxation. Projected area is estimated from the proxy bounding box, with a preferred minimum of 1.2%, a relaxed minimum of 0.2%, and an absolute maximum of 25%. Collision checks account for motion-dependent swept envelopes, while surface placement additionally enforces slope limits and four-corner footprint support. When no valid candidate is found, constraints are progressively relaxed and the search region is expanded. Water-related constraints remain enforced, and all relaxations are recorded in the resulting placement plan.

Baseline Implementation Details

Baseline Implementations. We adopt the official implementations of all baselines, adding Claude Opus 4.8 API adapters where necessary. The VLM (one-shot) and VLM (harness) baselines follow 3DCodeBench, with the latter supporting autonomous code execution and iterative refinement.

SigLIP2 Score Computation. All SigLIP2 scores in this paper (Table 1, Table 2, Table 4, Table 5, Table 7) are computed with google/siglip2-base-patch16-224 and reported as the cosine similarity between L2-normalized image and text embeddings, recovered from the model’s image–text logits via cos⁡=(logit⁡−b)/t\cos= (\operatorname{logit} - b)/t, where t=112.67t = 112.67 and b=−16.77b = -16.77 are SigLIP2’s learned temperature and bias. Scores are averaged over the four azimuth views at the object level (Table 1, Table 5, Table 7), the primary camera view at the scene level (Table 2), and the five variations per prompt in the diversity evaluation (Table 4).

Object-level Baselines. All methods receive identical text prompts and reference images, except BlenderRAG, which only supports text inputs. Each task has five Gemini-generated reference images: ref_00 is the conditioning reference provided to the generation methods, while ref_01–ref_04 are held out exclusively for evaluation (DINOv3) and the human study and are never shown to any method; the conditioning reference is never used for scoring. All assets are evaluated under identical camera, lighting, background, and rendering settings. Failed generations are retained as blank renders rather than excluded.

Scene-level Baselines. All scene-level methods receive identical text prompts and follow their respective official scene generation and camera selection pipelines. For InfiniDream, the Scene Picker automatically retrieves the most relevant initial coarse scene based on the input prompt from a library of 70 pre-generated environments (14 biomes with five seeds each), which is then used as the basis for subsequent procedural editing. The library was built before the benchmark prompts were finalized and was not modified afterwards. For reproducibility, the Scene Picker’s selection is made once per task, and the chosen base scene and seed are then fixed across all benchmark and ablation runs. The other baselines do not use this library. Code2Worlds selects from its available procedural factories even when no closely matching category exists.

Failure Handling. We resolve version- and environment-related execution issues wherever possible to ensure that baselines run as intended. We do not report a separate scene-level executability metric.

Ablation Study Details

Object-level Ablation. We provide additional implementation details for the six ablation settings: (1) w/o Ref. Image removes the reference image from the generation pipeline; (2) Random Factory replaces factory retrieval with random selection; (3) w/o Planner removes structured planning and replaces the Editing Plan with empty content; (4) Agent (Infinigen) gives a Claude Opus 4.8 coding-agent harness access to the entire Infinigen codebase and Bash tools, allowing autonomous code generation, rendering, and refinement without SCE constraints, for up to six rounds; (5) w/o Refiner removes fine-level refinement; and (6) 3 Rounds limits the refinement budget to three rounds, compared with six in the full method.

Scene-level Ablation. We evaluate four placement variants: (1) w/o Grid Search removes discrete semantic planning and grid-based placement, allowing the Director to predict continuous 3D coordinates directly; (2) w/o Anchoring replaces our seven anchoring types with Infinigen’s original grounding mechanism; (3) w/o Semantic Target removes semantic surface constraints from grid search, allowing placements on semantically inappropriate surfaces; and (4) Agent (Placement) provides a VLM agent with our generated 3D assets, the text prompt, and the same selected coarse scene, allowing autonomous placement without our placement pipeline.

Contact Spread. For each object, we select the lowest 12% of sampled points and cluster them into four contact regions using k-means. The lowest point in each region is selected, and its vertical gap to the underlying terrain is computed via ray casting. Contact Spread is defined as the difference between the maximum and minimum gaps, capturing uneven grounding independently of terrain elevation.

Both contact metrics are computed only for surface-supported objects (rooted, upright, tilted, conform, and afloat; afloat is measured against the water surface); airborne and submersed objects are excluded, while the Unreadable Rate covers all placed objects. Ground-contact Error uses the absolute gap, so floating and penetration both count by magnitude. Objects whose contact rays find no supporting surface, or that failed to populate, are excluded. P95 is the nearest-rank 95th percentile of per-object values pooled across all surface-supported requested-add objects in the 20 scenes. For Agent (Placement), anchoring types are inferred from the full-pipeline runs; objects without a counterpart, and plant assets whose lowest vertices are foliage rather than ground contacts, are excluded from its contact statistics.

Detailed Capability Comparison

We provide a detailed capability comparison of representative 3D generation methods in Table 8. The comparison covers three asset-level and five scene-level capabilities, highlighting InfiniDream’s integration of open-ended procedural asset generation with scalable world construction.

MethodNovel Proc. AssetsSeed DiversityProc. EditingText-to-ScenePlacement ReasoningSemantic AnchoringScalable PopulationAnimation
VIGA [110]✓×✓✓✓××✓
LL3M [55]✓×✓×××××
Infinigen [70]×✓✓×××✓✓
3D-GPT [82]×✓✓✓✓×✓✓
SceneX [119]×✓✓✓✓△✓✓
Code2Worlds [117]×✓✓✓✓×✓✓
WorldClaw [23]×××✓✓✓✓×
WorldGen [92]×××✓✓△✓×
InfiniDream✓✓✓✓✓✓✓✓

Table 8. Capability comparison of representative 3D generation methods. We compare three asset-level and five scene-level capabilities. “Novel Proc. Assets” denotes the creation of procedural generators beyond predefined factories, while “Proc. Editing” includes editable procedural representations. “Placement Reasoning” refers to semantic spatial planning, whereas “Semantic Anchoring” requires semantic support-surface selection and geometry-aware contact alignment. ✓ indicates explicit support, △ partial support, and × support not established under our criteria.

Human Evaluation Details

Study Setup. We conduct a forced-choice preference study on 10 object and 10 scene generation tasks. For each object, participants are shown the text prompt, a held-out reference image not provided to any generation method, and front-view renders (1024×10241024 \times1024) from six methods: InfiniDream, BlenderRAG, VIGA, VLM (one-shot), VLM (harness), and Infinigen.

For each scene, participants compare 5-second orbit videos (854×480854 \times480) from five methods: InfiniDream, VIGA, VLM (harness), Code2Worlds, and Infinigen. All results are presented without cropping or background normalization.

The object subset covers animals, marine life, plants, and natural props, while the scene subset spans seven distinct environment types.

Evaluation Protocol. As illustrated in Figure 17, all methods are anonymized, and both task order and method positions are randomized independently for each participant. Each task contains two forced-choice questions evaluating semantic alignment and visual quality.

Human evaluation interface showing object-level and scene-level comparisons

Figure 17. Human evaluation interface. Participants compare anonymized generation results and independently select their preferred method for semantic alignment and visual quality at both the object and scene levels.

For objects, participants select the result that best matches the description and reference image, and the result with the highest 3D quality. For scenes, participants evaluate text alignment and overall scene realism, considering object placement, naturalness, and visual artifacts.

Each participant completes 40 judgments across both levels. We recruit 30 participants, yielding 20 judgments per level per participant.

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