Generate Any Scene: Scene Graph Driven Data Synthesis for Visual Generation Training
Authors: Ziqi Gao
Weikai Huang
Jieyu Zhang
Aniruddha Kembhavi
Ranjay Krishna
Venue: ICLR 2026 Poster (2026)
Keywords: synthetic data
synthetic caption
scene graph
text-to-image generation
Primary area: datasets and benchmarks

Abstract: Recent advances in text-to-vision generation excel in visual fidelity but struggle with compositional generalization and semantic alignment. Existing datasets are noisy and weakly compositional, limiting models' understanding of complex scenes, while scalable solutions for dense, high-quality annotations remain a challenge. We introduce **Generate Any Scene**, a data engine that systematically enumerates scene graphs representing the combinatorial array of possible visual scenes. Generate Any Scene dynamically constructs scene graphs of varying complexity from a structured taxonomy of objects, attributes, and relations. Given a sampled scene graph, Generate Any Scene translates it into a caption for text-to-image or text-to-video generation; it also translates it into a set of visual question answers that allow automatic evaluation and reward modeling of semantic alignment. Using Generate Any Scene, we first design a self-improving framework where models iteratively enhance their performance using generated data. SDv1.5 achieves an average ***4%*** improvement over baselines and surpassing fine-tuning on CC3M. Second, we also design a distillation algorithm to transfer specific strengths from proprietary models to their open-source counterparts. Using fewer than 800 synthetic captions, we fine-tune SDv1.5 and achieve a ***10%*** increase in TIFA score on compositional and hard concept generation. Third, we create a reward model to align model generation with semantic accuracy at a low cost. Using GRPO algorithm, we fine-tune SimpleAR-0.5B-SFT and surpass CLIP-based methods by ***+5%*** on DPG-Bench. Finally, we apply these ideas to the downstream task of content moderation where we train models to identify challenging cases by learning from synthetic data.

Published as a conference paper at ICLR 2026
GENERATE ANY SCENE: SCENE GRAPH DRIVEN DATA SYNTHESIS FOR VISUAL GENERATION TRAINING
Ziqi Gao Weikai Huang'; JJeyu Zhang's Aniruddha Kembhavi", Ranjay Krishna
1 University of Washington, Allen Institute for Artificial Intelligence
O Code: /ttps://github. http://ggttuu...om/aaaaaaaa www..///l....aabeeeeeaaaaaaaaaaa
Dataset: AeySrate-nyyScene Dataset
ABSTRACT
Recent advances in text-to-vision generation excel in visual fidelity but struggle with compositional generalization and semantic alignment. Existing datasets are noisy and weakly compositional, limiting models' understanding of complex scenes, while scalable solutions for dense, high-quality annotations remain a challenge. We introduce GENERATE ANY SCENE, a data engine that systematically enumerates scene graphs representing the combinatorial array of possible visual scenes. GENERATE ANY SCENE dynamically constructs scene graphs of varying complexity from a structured taxonomy of objects, attributes, and relations. Given sampled scene graph, GENERATE ANY SCENE translates it into caption for text-to-image or text-to-video generation; it also translates it into a set of visual question answers that allow automatic evaluation and reward modeling of semantic alignment. Using GENERATE ANY SCENE, we first design a self-improving framework where models iteratively enhance their performance using generated data. SDv1.5 achieves an average 4% improvement over baselines and surpasses fine-tuning on CC3M. Second, we also design a distillation algorithm to transfer specific strengths from proprietary models to their open-source counterparts. Using fewer than 800 synthetic captions, we fine-tune SDvl.5 and achieve a 10 % increase in TIFA score for compositional and hard-concept generation. Third, we create a reward model to align model generation with semantic accuracy at a low cost, Using GRPO algorithm, we fine-tune SimpleAR-O.Ee A-...-----FFF- and surpass CLIP--asse methods by +5 % on DPG-Bench Finally, we apply these ideas to the downstream task of content moderation where we train models to identify challenging cases by learning from synthetic data.
INTRODUCTION
Despite the high-fidelity of modern generative models (text-to-image and text-to-video), we are yet to witness wide-spread adoption 2 (). Controllability remains out of reach (6) Generated
content appears realistic but often falls short of semantic alignment 7 R ICO. Users prompt models with specific concept in mind. For example, when prompted to generate a scene of "a black dog chasing after rabbit that eating grass, in Van Gogh's 's style, with starlight lightning", some models are likely to generate an image of a dog but might miss the rabbit or get the style incorrect.
We hypothesize that these limitations stem not only from architectural bottlenecks but more funda- mentally from the lack of structured, compositionally rich training data (3). especially those with uncommon compositions. Popular datasets such as LAION ( and CC3M (2) predominantly consist of web-crawled image-caption pairs, which are inherently noisy, weakly compositional, and biased toward single-object, coarse-graines descriptions. Such datasets lack explicit grounding of object-attribute relations and multi-object interactions, restricting models' ability to generalize to complex visual scenes. Efforts to enhance caption quality the compositional density and semantic richness of captions significantly improve generative per- formance. Nevertheless, manual curation of such dense compositional annotations is intensvnte while automatic annotation methods (e.g., via MIMs) suffer from hallucination and semantic noise.
B 3 have demonstrated that enhancing

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Published as a conference paper at ICLR 2026
Constructing compositional dataset requires that we first define the space of the visual content. Scene graphs are one such representation of the visual space (4 15 16 I7 18), grounded in cognitive science (I9). A scene graph represents objects in scene individual nodes in a graph. Each object is modified by attributes, which describe its properties. For example, attributes can describe the material, color, size, and location of the object in the scene. Finally, relationships are edges that connect the nodes. They define the spatial, functional, social, and interactive relationships between objects (20). For example, in living room scene, "table" node might have attributes like "wooden" or "rectangular" and be connected to a "lamp" node through a relation: "on top of". This systematic scene graph structure provides simple yet effective ways to define and model the scene. As such, scene graphs are an ideal foundation for systematically defining the compositional space of visual content text-to-vision generation.
We introduce GENER ANY SCENE, system capable of efficiently enumerating the space of scene graphs representing wide range of visual scenes. GENERATE ANY SCENE composes scene graphs of any structure using rich taxonomy of visual elements, translating each scene graph into an input caption and visual question answers to evaluate the output image or video. In particular, we first construct rich taxonomy of visual concepts consisting of 28, 787 objects, 1, 494 attributes, 10, 492 relations, 2, 193 scene attributes from various sources. Based on these assets, GENERATE ANY SCENE can synthesize an almost infinite number of scene graphs of varying complexity (21). Besides, GENERATE ANY SCENE allows configurable scene graph generation. For example, evaluators can specify the complexity level of the scene graph be generated or provide a seed scene graph to be expanded. By automating these steps, our system ensures both scalability and adaptability, providing researchers and developers with diverse, richly detailed scene graphs and corresponding captions tailored to their specific needs. We also conduct comprehensive text-to-vee text-to-vision evaluations using our generated captions, as detailed in Appendix A
We show that GENERATE ANY SCENE can allow generation models to self-improve Our diverse captions can facilitate a framework to iteratively improve Text-to- Vixion generation models using their own generations. Given a model, we generate multiple images, identify the highest-scoring one, and use it as new fine-tuning data to improve the model itself. We fine-tune SDv1.5 (22) and achieve an average of 4% performance boost compared with original models, and this method is even better than fine-tuning mun-tuning with the same amount of real images and captions from the Conceptual Captions CC3M over different benchmarks.
We also use GENERATE ANY SCENE to design targeted distillation algorithms. Using our evaluations, identify limitations in open-sourced models that their proprietary counterparts excel Next, we distill these specific capabilities from proprietary models. For example, Dal. 3 (3) excels particularly in generating composite images with multiple parts. We distill this capability into SDv1.5, effectively bridging the gap between DaLL-E 3 and SDv1.5. After targeted fine-tuning, SDv1.5 achieves a 10 % increase in TIFA score for compositional tasks and hard concept generation.
Then we propose a low-cost scene graph-based reward model for RLHF (44 in text-to-image generation. By leveraging synthetic scene graphs generated by GENERATE ANY SCENE, we generate exhaustive question-answer pairs that cover all objects, attributes, and relationships in the caption. Our method enables fine-grained, compositional reward modeling without manual annotation or heavy LLM inference. With GRPO (25), we fine-tune SimpleAR-0.5B--5 (26) using a scene graph reward model, achieving better compositional alignment than CLIP-based methods ( (+5 % on DPG-Bench (28)
Finally, we apply GENERATE ANY SCENE to the downstream application content moderation. Content moderation is a vital application, especially as Text-to- Vision generation models improve. A key challenge lies in the limited diversity of existing training data. To address this, we leverage GENERATE ANY SCENE to generate diverse and compositional captions, creating synthetic training data that complements existing datasets. By retraining a ViT-T (29) detector with our enriched dataset, enhance its detection performance, particularly in cross-model cross cross-dataset scenarios.
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2 GENERATE ANY SCENE
In this section, we present GENERATE ANY SCENE (Figure D. a data engine that systematically synthesizes diverse scene graphs in terms of both structure and content and translates them into corresponding captions.
Scene graph. A scene graph is a structured representation of a visual scene, where objects are represented as nodes, their attributes (such as color and shape) are properties of those nodes, and the relationships between objects (such spatial or semantic connections)
represented edges. In recent years, scene graphs have played a crucial role in visual understanding tasks, such as those found in Visual Genome (14) and GQA (30) for visual question answering (VQA). Their utility has expanded to various Text-to- ti-Vinioon generation tasks. For example, the DSG (31) and DPG (TO) benchmarks leverage scene graphs to evaluate how well generated images align with captions.
Taxonomy of visual elements. To construct a scene graph, we use three main metadata types: objects, attributes, and relations. We further introduce scene attributes that capture global visual contexts, such as art style, to facilitate comprehensive caption synthesis. The statistics and source of Additionally, we build a hierarchical taxonomy that categorizes
our metadata are shown in Table !| metadata into distinct levels and types, enabling fine-grained analysis. This structure supports precise content synthesis, from broad concepts like "flower" to fine-graings instances such as "daisy."
Table 1: Summary of the quantities and sources of visual elements. Details are in Appendix
B
Metadata Type
Number
Source
Objects
28,787
WordNet 32
Attributes
1,494
Wikipedia 33
etc.
Relations
10,492
Synthetic Visual Genome e1
Scene Attributes
2,193
Places365 (55
etc.
2.1 GENERATING DATA WITH SCENE GRAPHS
Step Scene graph structure enumeration. Our engine pre-computes a library of directed scene- graph topologies subject to user-specified structural constraints: complexity (total number of objects, relations, and attributes) (36), average node degree, and number of connected components. We first sample the number of object nodes and then systematically enumerate feasible edge sets and attribute attachments that satisfy these constraints. We provide 3 optional controls: (i) degree-profile bounds per-node in/out-degree, (ii) seed-graph preservation embeds user- user-provided seed graph subgraph of each enumerated structure, and (3) commonsense plausibility filtering prunes implausible contents while retaining compositional diversity (See Appendix. H.1 All enumerations performed once per parameter tuple and cached for fast querying.
Step 2: Populate the scene graph structure with metadata. Given generated scene graph structure, the next step involves populating the graph with metadata. For each object node, attribute node, and relation edge, we sample the corresponding content from our metadata. This process is highly customizable and controllable: users can define the topics and types of metadata to include, for instance, by selecting only commonsense metadata or specifying relationships between particular objects. By determining the scope of metadata sampling, we can precisely control the final content of the captions and easily extend the diversity and richness of scene graphs by adding new metadata.
Step 3: Sample scene attributes. We also include scene attributes that describe aspects such as the art style, viewpoint, time span (for video), and 3D attributes (for 3D content). These scene attributes are sampled directly from our metadata, creating a list that provides contextual details to enrich the description of the visual content.
Step 4: Translate scene graph to caption. We introduce a deterministic and programmatic algorithm that converts scene graphs with scene attributes into captions. It traverses scene graphs by converting bbjjectt/atribbuts//eelaiiio into descriptive text in topological order, while tracking each object's references to ensure coherence. Programmatic grammar rules are employed (e.g.. disambiguating identical objects with "the first/second" and skipping already mentioned objects) prevent duplication and misreference, resulting in clear captions. We also provide LLM paraphrasing as an optional step diversify wording; however, studies (see Appendix A.3) show that paraphrasing does not.
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Step graph structure query Graph
Wenther encayy sandy- Lightig
Object
Derting
Dog:
Flaver
doogh Cougren Tacherquas
Altrbate
Shapp: Squame
Teatare: Crvanyy
Styl
paintigg..
Spetist Next io Beyond. Syribllic represets
Comsera Contig
Vieut ege Labal, lip shot.
Rlalefornnip
[Video Orly)
Structure contige
Categoriee
Aftributes Number
Configune Semping
Artibures
Rubbe
dog le rabiit thet le eesting Gogllt
Program
Syrthete Coptions
Step 4: Tronslate seene gruph and scene uttrihutes to cuptian
Scare Origha
What dag chase star? inege?
The Mock
You
und rnward modklngg
(ng. Complenty 0.8)
Arrbu Cb
Ranios
Step & Convert sumne graph to series question-smmeerr poire
The generation pipeline of GENERATE ANY SCENE, Step 1: Enumerate diverse scene graph structures under user-defined constraints. Step 2: Populate structures with sampled objects, attributes, and relations. Step 3: Sample scene attributes such as style, perspective, or time span. Step 4: Translate scene graph and attributes into coherent captions. Step 5: Automatically generate QA pairs covering all elements for evaluation and reward modeling.
Figure 1:
materially affect results. We adopt the programmatic caption converter as the default for speed and low hallucination rate.
Step Convert scene graph to a series of question-answer pairs. Given a synthetic scene graph, GENERATE ANY SCENE automatically enumerates exhaustive QA pairs using templates that query object attributes (e.g., What color is the sphere?), spatial relations (e.g.. What is to the left of the cube?), and other compositional elements. Each answer maps directly to object, attribute, or edge, ensuring full coverage of the graph at minimal cost. This enables both VQA-basee evaluation of generated images and the construction of fine-graie fine-grained reward models without manual labeling costly LLM inference.
3 I MODELS WITH SYNTHETIC CAPTIONS
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(c) GenAi- Bench Image
(4) GenAi- Bench Video
Figure 2: Results for Self- dell-limpoving Models. Average VQA score of SDv1.5 fine-tuned different data across IK GENERATE ANY SCENE image/video evaluation set and GenAI-Bench image/vides benchmark (7).
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With GENERATE ANY SCENE, develop a self-improvemmnt framework to improve generative capabilities. By generating scalable compositional captions from scene graphs. GENERATE ANY SCENE expands the textual and visual space, allowing for diversity of synthetic images that extend beyond real-world scenes. Our goal is to utilize these richly varied synthetic images to further boost model performance.
Iterative self-improving framework. Inspired by DreamSync 33 we designed an iterative self- improving framework using GENERATE ANY SCENE with SDv1.5 as the baseline model. With VQA Score, which shows strong correlation with human evaluations on compositional images (3)), we guide the model's improvement throughout the process. Specifically, GENERATE ANY SCENE generates 3 x 10K captions across three epochs. For each caption, SDv1.5 generates 8 images, and the image with the highest VQA Score selected. From each set of 10K optimal images, we then select the top 25% (2.5K image-caption pairs) as the training data for each epoch. In subsequent epochs, we use the fine-tuned model from the prior iteration to generate new images. We employ LoRA 440) for parameter-efficient fine-tuning.
Baselines. We conduct comparative experiments with the CC3M dataset, which comprises high- quality and diverse real-world image-caption pairs (12). We randomly sample 3 x 10K captions from CC3M, applying the same top-score selection strategy for iterative fine-tuning of SDv1.5. Additionally, include baseline using mandom-sample fine-tuning strategy validate the advantage of our highest-scoring selection-basee strategy. We evaluate our seff-improving pipeline on Text-to-Vision generation benchmarks, including GenAl Bench (37). For the Text-to- -idio generation task, Tex12Video-Ze as the baseline model, substituting its backbone with the original SDv1.5 and our fine-tuned SDvI.5 models.
Table Quality and diversity comparison on GenAT-Bench. Fine-tuning with GENERATE ANY SCENE captions improves global semantic fidelity and perceptual quality without reducing generation diversity.
Table Generalization to unseen composi- tions. On a 400-caption test set containing only unseen combinations of seen elements, the model fine-tuned with GENERATE ANY SCENE achieves the best compositional generalization.
SDv1.5
CC3M-FT
GAS-FT
SDv1.5 CC3M-FT GAS-FT
CLIPScore 0.3167
0.3196
0.3206
VQAScore 0.5823
0.6044
0.6109
RewaeR
0.2056
0.3842
0.3927
CLIPScore
0.2876
0.2927
0.2938
LPIPS
0.7297
0.7356
0.7329
ImageReward
0.4861
0.2602
-0.2497
Fine-tuning with our synthetic captions can outperform fine-tuning with high-quality real-world image-caption data. Our results show that fine-tuning with GENERATE ANY SCENE-generater synthetic data consistently outperforms CC3M-based fine-tuning across Text-to-Vistee generation tasks (Figure 2). achieving the highest gains with highest-scoring selection strategy. This highlights GENERATE ANY SCENE'S scalability and compositional diversity, enabling models to effectively capture complex scene structures.
In Table we further evaluate SDv,.5, the CC3M-finetuned model, and the model finetuned with GENERATE ANY SCENE captions on additional metries from GenAl-Bench. Fine-tuning with GENERATE ANY SCENE yields higher CLIPScore and ImageReward while preserving LPIPS, demonstrating that our method not only strengthens compositional alignment but also improves global semantic fidelity and perceptual quality without reducing generation diversity.
In Table we additionally evaluate whether our self-improving framework enhances combinatorial generalization. We extract all objects, attributes, and relations from the CC3M fine-tuning data and retain the metadata sampled by GENERATE ANY SCENE. Using the same element set as in the fine-tuning data, we synthesize 200 CC3M-element-baasee and 200 GENERATE ANY SCENE-element- based captions while excluding all seen combinations, forming a 400-caption test set of unseen compositions. The model fine-tuned with GENERATE ANY SCENE achieves the highest VQAScore, CLIPScore, and ImwgeReward, indicating stronger compositional generalization than both 1.v and the CC3M-finetnned baseline. Additional experiment settings and results in Appendix C
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Figure 3: Examples for Distilling Capabilities. Examples of images generated by DaLL-E 3, the original SDv1.5, and the fine-tuned versions. The left four captions demonstrate fine-tuning with multi-object captions generated by GENERATE ANY SCENE for better compositionality, while the right two columns focus on understanding hard concepts.
4 DISTILLING TARGETED CAPABILITIES
Although self-improving with GENERATE ANY SCENE shows clear advantages over high-quality real-world datasets, its efficiency is inherently limited by the model's s own generation capabilities. To address this, we leverage the taxonomy and systematical generation capabilities within GENERATE ANY SCENE to identify specific strengths of proprietary models (DaLL-E 3), and distill these
capabilities into sourcs models. More details are in Appendix D
We evaluate multiple models using captions controllably generated by GENERATE ANY SCENE and observe that Dal.--E 3 achieves TIFA Score 1.5 to times higher than those of other models. As shown in Figure 4a| when comparing TIFA Score across captions with varying numbers of elements (objects, relations, and attributes). Dal.L-E 3 counterintuitively maintains consistent performance regardless of element count. The performance of other models declines as the element count increases, which aligns with expected compositional challenges. We suspect that these differences are primarily due to DaLL-E 3's advanced capabilities in compositionality and understanding hard concepts, which ensures high faithfulness across diverse combinations of element types and counts.
Distilling compositionality from DaLL-E 3. When analyzing images generated from our synthetic captions, find that Dal.I-E 3 tends to produce straightforward combinations of multiple objects (Figure 5). In contrast, open-source models like SDvV.5 often omit objects from the captions, despite being capable of generating each one individually. This difference suggests that DaLL-E 3 may benefit from training data emphasizing multi-object presence, even without detailed layout or object interaction. Such training likely underpins DaLL-E 3's stronger performance on metrics like TIFA Score and VQA Score that prioritize object inclusion. To effectively distill these compositional abilities into SDv1.5, we employ GENERATE ANY SCENE for targeted synthesis of 778 multi-object captions, paired with images generated by Dal.- 3, for finetuning SDv1.5.
Distilling hard concepts understanding from DaLL-F Figure shows that Dal.I-E is capable not only of handling multi-object generation but also of understanding and generating rare and hard concepts, such as a specific species of flower. We attribute this its training with proprictary real-world data. Using the taxonomy of GENERATE ANY SCENE, we evaluate both models 10K GENERATE ANY SCENE captions that broadly cover the taxonomy. For each concept, we gather all captions in which it appears and average their generation scores obtain concept-level score for each model. Comparing these concept-level scores lets us identify the 81 concepts where SDv1.5 shows the largest gap relative Dal.L-E the full list is provided in Appendix D For distilling,

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increase the sampling frequency of these hard concepts and generate 778 captions incorporating these hard concepts with other elements, and use DaLL-E 3 to produce corresponding images.
Baselines, For the baseline, randomly synthesize 778 captions using GENERATE ANY SCENE paired with DaLL-E 3-generated images to fine-tune the model. To evaluate model improvements, generate another IK multi-object captions and 1K hard-concept captions separately.
Targeted caption synthesis via GENERATE ANY SCENE enables effective distillation of composi- tional abilities and hard concept understanding. We analyze images generated by SDv1.5 before and after fine-tuning on high-complexity captions (Figure Bb. Surprisingly, with fewer than 1K LoRA fine-tuning steps, SDv1.5 effectively learns DaLL-E is capability arrange and compose multiple objects within single image. Quantitatively. Figure Ab shows a 10% improvement in TIFA Score after targeted fine-tuning. surpassing the performance of the randomly fine-tuned model. On broader set of 10K GENER ATE ANY SCENE-generated captions, the targeted fine-tuned model consistently outperforms randomly fine-tuned and original counterparts across complex scenes (Figure 4a These results confirm not only the effectiveness but also the scalability and efficiency of GENERATE ANY SCENE. Also, the results in Figure 4c show that our targeted fine-tuning with hard concepts leads to improved model performance, reflected in higher average scores across captions and increased scores for each challenging concept.
REINFORCEMENT LEARNING WITH A SYNTHETIC REWARD FUNCTION
Reinforcement Learning with Human Feedback (RLHF) has become increasingly popular fine- tuning strategy in text-to-image generation 41 42 26 However, defining an effective reward model that accurately captures semantic alignment for text-to-image generation remains open challenge. Existing reward models like CLIP offer only roarse-grained image-text similarity signals, which fall short in assessing compositional correctness and lack interpretability. Alternative approaches have explored using visual question answering (VQA) proxy for evaluating semantic alignment, aiming for imer-grained assessments, yet require either iabor-intensive datasets with dense annotations or large volumes of contextually relevant questions via advanced LLMs. Leveraging its structured scene graph synthesis capabilities, GENER ATE ANY SCENE offers a scalable alternative by producing exhaustive semantic queries with negligible overhead, enabling low-cost, compositional reward modeling (Sec
Experiment setup. Building this scene graph-based reward modeling strategy, adopt Group Relative Policy Optimization (GRPO) as our reinforcement learning algorithm. We fine-tune the Ai-pleAR-O0.BB-FF model for epoch using 10K captions generated by GENERATE ANY SCENE, each paired with their scene graph-derived QA sets. For reward evaluation, we use Qwen2.5-VL-33.. a lightweight open-source vision-language model, answer these QA pairs given the model-generated
Modch
0.45
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Average TIFA Score
A.35
0,315
6,300
333
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Gate
0.30
Thrgetooly Fine tuned 5Dv1.
Avg. over Captions Arg. over Haard Concapte Typa
Object
(a) Distilling compositionality (b) Distilling compositionality from DaLL-E 3: Model results on
(c) Distilling hard concepts under- standing from DALL-E 3: Models' average TIFA Score performance over captions and hard concepts in 1K hard concepts GENERATE ANY SCENE captions.
from DaLL-E 3: Model results on TIFA vs. total element numbers in captions in IK multi-object GENERATE ANY SCENE captions.
TIFA vs. total element numbers in captions in 10K general GENERATE
ANY SCENE captions. ("Best
Open-Source Model" refers to Flux.1-schnell)
Figure 4: Results for Distilling Capabilities. The left two figures show the results for Distilling compositionality, while the rightmost figure shows the results for Distilling hard concepts under- standing from DALL-E3.
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Figure 5: Comparison of generated images. Our reward model enables image generation with better semantic alignment, realism, and visual quality than baselines.
images. The reward is computed as the accuracy across all questions. This fine- tne-grained, scene graph-aligned reward provides precise feedback on compositional faithfulness. As baseline, compare against SimpleAR-0.8
trained with CLIP-based rewards on 11K captions from real world datasets for one epoch. We evaluate our scene graph-based reward model on three benchmarks:
OPG-Bench (1), GenEval 4 and GenAI-Bench (3).. More details are in Appendix
GENERATE ANY SCENE rewards outperform CLIP. As shown in Table 4 our method outperforms both SFT and CLIP-RL models and achieves a significant improvement, demonstrating superior compositional faithfulness driven by explicit scene graph rewards. Importantly, this performance gain directly enabled by the GENERATE ANY SCENE engine, which constructs explicit scene graphs generate compositional captions. GENERATE ANY SCENE provides structured and cognitively aligned visual representation, from which we derive exhaustive QA pairs with minimal additional cost. Combined with lightweight VLM judge, this approach offers a scalable, low-cost solution for semantic- reward modeling.
Table 4: Evaluation on the DPG, GenEval and GenAI benchmark. GRPO training with our reward model outperforms both SFT baseline and CLIP-RL models. TO: two objects, P: position, CA: color attribute.
DPG-Bench
GenEval
GenAl-Bench
Method
Global
Relation
Overall
TO
P
Overall
Basic
Advanced
SimpleAR-O.5B-SFT
85.02
86.59
78.48
0.73
0.22
0.23
0.53
0.74
0.60
0.66
SimplcAR-O.5B-- (Clip)
86.64
88.51
79.66
0.82
0.26
0.38
0.59
0.75
0.60
SimplcAR-0.5B-EL. (Ours)
88.46
90.13
80.50
0.81
0.31
0.38
0.61
0.75
0.61
0.68
IMPROVING GENERATED-CONTEN DETECTION
Advances in Text-to- Text-lo-Vision generation underscore the need for effective content moderation Major challenges include the lack of high-quality and diverse datasets and the difficulty of generalizing detection across models Text-to- Vision generation 44 45). GENERATE ANY SCENE addresses these issues by enabling scalable, systematical generation of compositional captions, increasing the diversity and volume of synthetic data. This approach enhances existing datasets by compensating for their limited scope-fron from realistic to imaginative-an variability.
4
Experiment setup. To demonstrate GENERATE ANY SCENE'S effectiveness in training generated content detectors, we used the D dataset (46) as a baseline. We sampled 5K captioned real and SDv1.4 1.geer-geee image pairs from 3 and generated 5K additional images with GENERATE ANY
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SCENE captions. We trained a ViT-T 47 model with single-layer linear classifier and compared models trained with samples solely from D2 against those trained with samples from both GENERATE ANY SCENE and D
GENERATE ANY SCENE improves generated content detectors. We evaluate the detector's generalization the Genlmage (48) validation and images generated using GENERATE ANY SCENE captions. Figure demonstrates that combining GENERATE ANY SCENE-generated images with real-world captioned images consistently enhances detection performance, particularly across cross-model scenarios and diverse visual scenes. More details are in Appendix
Average F1 Scooe
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(c) Out of domain: Average de-
Model SD v1.4): Detection results on images generated SD v1.4
tection results on images generated by multiple models using captions
images generated multiple mod- els using our captions.
using the Genlmage dataset.
from the GenImage dataset.
Figure 6: Results for Application 4: Generated content detector. Comparison of detection performance across different data scales using D alone versus the combined D3 + GENERATE ANY SCENE training set in cross-model and cross-dataset scenarios.
7 COMPREHENSIVE EVALUATION WITH GENERATE ANY SCENE
Beyond showcasing GENERATE ANY SCENE in model training, we also show that GENERATE ANY SCENE is a valuable resource for comprehensive and compositional evaluation. Specifically, we synthesize 10K captions for text-to-image. 10K for text-to-videg and 1K for text-to-3D, covering diverse scene structures and content topics. We evaluate 12 text-to-image, 9 text-to-video, and text- to-3D models. Evaluations combine GENERATE ANY SCENE synthetic scene graphs with existing metrics (e.g., CLIP Score (49), VQA Score 199, TIFA Score 23 31) to assess semantic similarity, faithfulness, and human preference alignment. Our key findings include: (1) DiT-backbone text-to- image models align more closely with input captions than UNet-backbone models. (2) Text-to-video models struggle with balancing dynamics and consistency, while both text-to-video and text-to-3D models show notable gaps in human preference alignment. Except for aggregating quantitative results, we also leverage GENERATE ANY SCENE 's controllable captioning to evaluate models on fine-grained factors: perplexity, scene complexity, commonsense reasoning, and content category variation for case study.
Overall, GENERATE ANY SCENE yields stable, human-aligned rankings across T2//T2V/T23DD.
Through broad, controllable coverage of objects, attributes, relations, and categories, it serves as compositional stress test that reliably exposes plausibility gaps, category brittleness, and long-tail concept failures in current models (see Appendix A).
RELATED WORK
Text-to- Vision generation models. Text-to-lmage generation advances are driven by diffusion models and LLMs. Some open-source models (22 50) 51
52
53 54) use UNet backbones to
refine images iteratively. In parallel, Diffusion Transformers (DiTs) architectures(55) 56 57 58
have emerged as a better alternative in capturing long-range dependencies and improving coherence. Proprietary models like 3 ( and Imagen 3 (59) still set the ofate-of-the-an Based on Texi-to- Image generation method, Text-fo- Video generation models typically utilize time-aware architectures to ensure temporal coherence across frames (60) 61 62) (53] (64] 65 66. (7). In Text-to-3D generation,
recent proposed models 4 58 69 integrate the diffusion models with Neural Radiance Fields

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(NeRF) rendering to generate diverse 3D objects. Recent studies 266
12
have also explored
the integration of image generation into a unified multimodal language model (MLM) framework based on auto-regressive transformer architectures, demonstrating promising improvements in both performance and efficiency.
Synthetic captions for Text-to- generation. Captions for Text-to -oott-to-ui Vision generation models vary greatly in diversity, complexity, and compositionality. This variation makes it challenging and costly to collect large-scale and diverse captions written by humans. Consequently, synthetic captions have been widely used for both training (74, 38
75 76.
& 77
78
79) and evaluation
purposes Z7. For example, training methods like generated captions to enhance the model's understanding and alignment with human instruction. For evaluation, benchmarks such as T21-CompBench (7) and 12V-CompBench (8) utilize benchmarks generated by LLMs. However, LLMs hard to control and may introduce exhibit systematic bias. In this work, we propose a programmatic scene graph-base data engine that can generate infinitely diverse captions for improving Texi-lo-Vision generation models.
Diffusion (74 leverage I.I.M-
Finetuning techniques for Text-to- Vision generation. To accommodate the diverse applications and personalization needs in text-to- to-vision models, numerous fine-tuning techniques have been developed. LoRA (40) reduces tine-tuning costs via low-rank weight updates, while Textual Inver- sion (8) ST introduces new word embeddings for novel concepts without altering core parameters. DreamBooth (82) adapts models to specific subjects or styles using few personalized images, and
DreamSyne 38 enables models to self-improve by learning from their own high-quality outputs. Recently, RLHF (26 41 42) in Text-to- Vision generation has shown promise efficient fine- tuning strategy. In this work, we use several fine-tuning techniques with GENERATE ANY SCENE to improve Text-to- Vision generation models.
CONCLUSION
We present GENERATE ANY SCENE, a system leveraging scene graph programming to generate diverse and compositional synthetic captions for Text-to- Vision generation tasks. extends beyond existing real-world caption datasets include comprehensive scenes and even implausible scenarios. To demonstrate the effectiveness of GENERATE ATE ANY SCENE, we explore four applications: (I) reff-improvement by iteratively optimizing models, (2) distillation of proprictary model strengths into open-source models, (3) scene-gre scene-graph-bee graph-based efficient reward model within the GRPO, and (4) robust content moderation with diverse synthetic data. GENERATE ANY SCENE highlights the importance of synthetic data in improving Text-fo- Vision generation, and addresses the need to systematically define and scalably produce the space of visual scenes.
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A EVALUATING Text-to-Vision generation MODELS WITH GENERATE ANY SCENE
A.1 EXPERIMENT SETTINGS
Models. We conduct experiments on 12 image models (54, 50
55
56
57
58
Text-to- Video models K3
and Text-to-31 models (68.
For Text-to-Image generation, select range of open-source models, including those utilizing UNet backhones, such as DeepFloyd IF (54), SDv2./ (22), SDXI (50). Playground v2.5 ( and Wuerstchen v2 (52), as well as models with DiT backbones, including SD3 Medium (55), PixArt-a (66), PixArt- (57), I-schnll (58) FLUX. (58), and
FLUX 1. Closed-source models, such as DaLL-E 3 (3) and FLUXI.1 PRO (58), are also
assessed to ensure comprehensive comparison. All models are evaluated at a resolution of 1024 x 1024 pixels.
For Text-to- Video generation, select nine open-source models: ModelScope (3). ZeroScope (3). Texe (62), CogVideoX-28 (66), VideoCrafier2 (65) Ani- mateLCM (6.. AnimateDiff (60). Freelnit (64) and Open-Sora 1.2
67). We standardize the frame length to 16 across all video models for fair comparisons.
For Text-to-3D generation, we evaluate five recently proposed models: SJC (69) Dream- Fusion (68), Magic31) (71), Latent-NeRF (70), and ProlificDreamer ④ We employ the implementation and configurations provided by ThreeStudio (84) and generate videos by rendering from 120 viewpoints. To accelerate inference, we omit the refinement stage. For Magic3D and DreamFusion, we respectively use DeepFloyd IF and SDv2.1 as their 2D backbones.
Metrics. Across all Text-to-Visiee generation tasks, Clip Score (49) (semantic similarity), VQA Score (39) (faithfulness), TIFA Score (23 3T) (faithfulness), Pick Score (85) (human preference),
and ImageReward Score (86) (human preference) as general metrics:
Clip Score: Assesses semantic similarity between images and text.
VQA Score and TIFA Score: Evaluate faithfulness by generating question question-answer answer pairs and measuring answer accuracy from images.
Pick Score and ImageReward Score: Capture human preference tendencies.
We also use metrics in VBench (87 to evaluate Texi-to-Video generation models on fine-grained
dimensions, such as consistency and dynamics, providing detailed insights into video performance.
For fexieto-Video generation and Text-to-3D generation tasks:
We calculate Clip Score, Pick Score, and ImageReward Score on each frame, then average these scores across all frames to obtain an overall video score.
For VQA Score and TIFA Score, we handle Text-to-Viee Video generation and Text-to-3D genera- tion tasks differently:
In Texi-to- Video generation tasks, we uniformly sample four frames from the f6-frame sequence and arrange them in 2 x 2 grid image.
For Text-to-3D generation tasks, we render images 45-degree intervals from nine different viewpoints and arrange them in a 3 x 3 grid.
This sampling approach optimizes inference speed without affecting score accuracy 39
Synthetic captions. We evaluate our Text-to-Image generation and Text-to- Text-to-Video generation models on 10K randomly generated captions, with scene graph complexity ranging from to 12 and scene attributes from 0 to 5, using unrestricted metadata. The captions exhibit an average graph degree of 1.15, with values spanning from 0.0 to 0.8. The mean number of connected components per scene graph is 3.51, ranging from 1 to 11. For Text-to-3D generation models, due to their limitations in handling complex captions and time-intensio generation, we restrict scene graph complexity to 1-3, scene attributes to 0-2, and evaluate IK captions.
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0.4
0.21
TIFA Score
Pick Score
0.3
*
0.20
0.2
0.70
0.5
Image Reward Score
0.65
WO
0.0
VQA Score
0.60
-0.5
0.55
-1.0
0.50
DIT
UNet
DIT
UNet
Backbone
Backbone
Models
Stable Diffusion Medium
FLUX. 1-sch-ls
SDXL Wuerstchen DeepFloyd IF
PixArt-a
FLUX1.1
PixArt-E
Playground v2.5
FLUX11ddev
0
Stable Diffusion v2-1
DALL-E 3
Figure 7: Comparative evaluation of Text-to-Image generation models across different backbones (DiT and UNet) using multiple metrics: TIFA Score, Pick Score, VQA Score, and ImageReward Score.
A.2 OVERALL RESULTS
We evaluate Text-to-lmage generation, Text-to-Videe Video generation, and Text-to-3D generation models on GENERATE ANY SCENE.
Table 5: Overall performance of Text-to-lmage generation models over 10K GENERATE ANY SCENE captions. 'Evaluated on KK caption subset due to inference cost constraints.
Model
clip score
pick score
yqa score
tifa score
image reward score
Playground v2.5
51
0.2581
0.2132
0.5734
0.2569
0.2919
Stable Diffusion v2-1
0.2453
0.1988
0,5282
0,2310
-0,9760
SDXL
0.2614
0,2046
0.5328
-0.3463
Wuerstchen v2 52
0,2448
0.2022
0,5352
0,2239
-0,3339
DeepPloyd IF XL
0,2396
0.1935
0.5397
0,2171
-0.8687
Stable Diffusion 3 Medium (55)
0.2527
0.2027
0.5579
0.2693
-0,0577
PixArt-a (56
0.2363
0.2050
0.6049
0.2593
0.1149
PixAnt-) 57
0.2390
0.2068
0.6109
0.2683
0.0425
FLUX.1-den
0.2341
0.2060
0.5561
0,2295
0.1588
FLUX.1-schee
0.2542
0.2047
0.6132
0,2833
0.1251
F PRO 58
0.2315
0.2065
0.5744
0.2454
-0.0361
Dalle-3 4
0.2518
0.2006
0.6871
0.4249
0.3464
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Text-to-Image generation results. (Figure
Table
1. DiT-backbone models outperform UNet-backbone models on VQA Score and TIFA Score, indicating greater faithfulness and comprehensiveness to input captions.
2. Despite using a UNet architecture. Playground v2.5 achieves higher Pick Score and Im- ageReward Score scores than other open-source models. We attribute this to Playground v2.5 alignment with human preferences achieved during training.
3. The closed-source model DaLL-E 3 maintains significant lead in VQA Score, TIFA Score, and ImageReward Score, demonstrating strong faithfulness and alignment with captions across generated content.
Texi-to-Vides generation results. (Table
Table Overall performance of open-source Text-to- -Video generation models over 10K GENERATE ANY SCENE captions. Red Cell is the highest score. Yellow Cell the second highest score. Crore.CCloo CClose- source models evaluated on 1K caption subset due to high inference cost.
image reward score
Model
clip score
pick score
VQA score
TiFA score
VideoCraft2 (65)
0.2398
0.1976
-0.4202
0.5018
0.2466
AnimateLCM 6
0,2450
0,1987
-0.5754
0.4816
0.2176
AnimateDiff (60)
0.2610
0,1959
-0.7301
0.5255
0.2208
Open-Sora 1.2 66
0.2259
0.1928
-0.6277
0.5519
0.2414
FreeInit 649
0.2579
0.1950
-0,9335
0.5123
0.2047
ModelScope 633
0,2041
0,1886
-1.9172
0.3840
0.1219
Text2Video-Zee 62
0.2539
0.1933
-1.2050
0.4753
0.1952
Cog VogVideoX-2B (66
0.2038
0.1901
-1.2301
0.4585
0.1997
ZeroScope [X
0.2289
0.1933
-1.1599
0.4892
0.2388
KLING 1.6 [8)
0.2215
0.1985
-0.3419
0.5307
0.2802
Wanx 2.1 88
0.2308
0.1969
-0.1418
0.5970
0.3328
Table 7: Overall performance of open-source Text-to-Video generation models over 10K GENERATE
ANY SCENE captions with VBench metrics. Red Cell the highest score. Blue Cell the lowest score.
subject consistency
background consistency
motion smoothness
dynamic degree
aesthetic quality
imaging quality
Model
Open-Sora 1.2
0.9964
0.9907
0.9973
0.0044
0.5235
0.6648
Text2Vicco-eero
0.8471
0,9030
0.8301
0.9999
0.4889
0.7018
VideoCraft2
0,9768
0.9688
0.9833
0.3556
0,5515
0.6974
AnimateDiff
0,9823
0.9733
0.9859
0.1406
0.5427
0.5830
FreeInit
0,9581
0.9571
0.9752
0.4440
0.5200
0.5456
ModelScope
0,9795
0,9831
0.9803
0.1281
0.3993
0.6494
AnimateLCMM
0,9883
0.9802
0,9887
0.0612
0.6323
00.9977
Cog X2
0,9583
0.9602
0,9823
0.4980
0.4607
0.6098
ZeroScope
0.9814
0.9811
0.9919
0.1670
0.4582
0.6782
Open-source text-to-video models face challenges in balancing dynamics and consistency (Table 7). This is especially evident in Open-Sora 1.2, which achieves high consistency but minimal dynamics, and Text2Video-Zero, which excels in dynamics suffers from frame inconsistency.
2. All models exhibit negative ImageReward Score (Table 6), suggesting a lack of human- preferred visual appeal in the generated content, even in cases where certain models demon- strate strong semantic alignment.
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3. As expected, SOTA close-souree text-to-vides models outperform others overall, particularly in image reward, VQA score, and TIFA score. This indicates their superior alignment with human preferences, as well as stronger faithfulness and compositional capabilities in generation.
4. Among open-source models, VideoCrafter2 strikes balance across key metrics, leading in human-pres prefaeeeefeeence alignment, faithfulness, consistency, and dynamic.
Text-to-3D generation results. (Table
Table 8: Overall performance of Text-to-DD generation models over IK GENERATE ANY SCENE captions. 'Evaluated on 100 caption subset due to high inference cost.
image reward score
Model
clip score
pick score
vqa score
tifa score
Latent-NeRF
70
0.2115
0.1910
0.4767
0.2216
-1.5311
DreamFusion-Se
(68
0.1961
0.1906
0.4421
0.1657
-1,5582
Magic3D-sd
0.1947
0.1903
0.4193
0.1537
-1.6327
SJC (69)
0.2191
0.1915
0.5015
0.2563
-1,4370
DreamFusion-IH
(68
0.1828
0.1857
0.3872
0.1416
-1.9353
Magic3D-IF
0.1919
0.1866
0.4039
0.1537
-1.8465
ProlificDreamer
0.2125
0.1940
0.5411
0.2704
-1.2774
Mcshy-4 90
0.2163
0.1922
0.5290
0.2908
-1.0496
1. Among open-source models, ProlificDreamer outperforms other models, particularly in ImageReward Score, VQA Score and TIFA Score.
2. All models receive negative ImageReward Score scores, highlighting significant gap between human preference and current Text-to-3D generation generation capabilities.
3. Meshy-4 demonstrates overall superior performance compared to all open-source models, especially terms Clip Score, TIFA Score and ImageReward Score, reflecting its strengths in semantic generation and human preference alignment.
A.3 VALIDATION OF PHRASING ROBUSTNESS AND HUMAN ALIGNMENT
To assess robustness linguistic variation and verify that automated metries reflect human preferences, we conduct two focused studies.
A.3.1 PHRASING ROBUSTNESS VIA PARAPIRASING
Setup. We sample 100 scene graphs from the 10K benchmark while preserving the distribution of object counts, relation density, and attribute complexity. For each graph, GPT-4o generates linguistically varied yet graph-faithful caption using the prompt below.
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Paraphrasing Prompt
You are given a scene graph JSON format, where:
"nodes" contain objects and their attributes, "edges" describe relationships between objects link attributes to objects.
Your task:
Understand the semantic meaning of each node and edge.
2. Convert the graph into a natural language caption that describes
the entire scene.
3. Include all objects, attributes, and relations from the graph,
and strictly follow the graph structure.
4. Do not introduce new objects or relationahips not present the graph.
Input: (scene_graph)
We then re-score all models with VQA Score under these paraphrased captions. Results listed in Table y
Table Paraphrase robustness: VQA Score and ranks on 100 graphs.
Model
Orig. Score
Para. Score
Diff
Orig. Rank
Para. Rank
DALLE-3
0.6871
0.7542
+0.0671
ILUX.I-schnel
0.6132
0.6648
+0.0516
PixArt-E
0.6109
0.6159
+0.0050
PixArt-a:
0.6049
0.6043
-0.0006
Playground v2.5
0.5734
0.5075
-0.0659
Stable Diffusion 3
0.5579
0.5140
-0.0439
FLUX. I-dev
0,5561
0.5024
-0.0537
DeepFloyd IF XL
0.5397
0.5606
+0.0209
Wuerstchen v2
0.5352
0.5014
-0.0338
10
SDXL.
0.5328
0.5322
-0.0006
SD v2-1
0.5282
0.4961
-0.0321
11
11
Findings. The Pearson correlation coefficient between model rankings on programmatic versus paraphrased captions is 0.9232, indicating very strong positive correlation.
This validation study demonstrates strong consistency between the two approaches. Importantly, the top-performing models (DaLL-E FLUXXI-schnell. PixArt-E, PixAri-a) maintain their rankings across both evaluation conditions, while the relative ordering of models remains largely consistent. This high correlation validates that our programmatic approach produces rankings that are gener- alizable and not artifacts of the templated caption generation. The slight variations observed (c.g.. some mid-tier models showing small rank changes) are within expected bounds and do not affect the overall conclusions about model capabilities.
A.3.2 HUMAN ALIGNMENT STUDY
Setup. We evaluate six representative models (DaLL-E 3, FLUX.I-schnell, PixArt-E, Playground v2.5, SD3 Medium, SDv2.1) I) with diverse performance characteristics and recruit human evaluators. Three independent evaluators each assess 40 caption- groups, with 10 shared overlapping groups across all evaluators to measure inter-annotator agreement. Evaluators ranked the generated images based both relevance to the caption and overall visual quality. We show the rankings in Table I0
Findings
Inter-annotator reliability The 3 evaluators showed strong agreement on the 10 shared samples, with Spearman correlation coefficient of 0.962, demonstrating consistent human judgment criteria.
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Table 10: Human vs. VQA rankings (lower better).
Model
VQA Rank
Human Avg. Rank
DaLL-E
FLUX.I-schnel.
2
2
PixArt-L
Playground v2.5
4
3
SD3 Medium
SDv2.
6
6
Human-man metric alignment. The correlation between human rankings and our VQA Score rankings is 0.918, indicating strong alignment between automated and human evaluation:
This study validates that VQA Score-based rankings closely align with human preferences. The consistency between automated metrics and human judgment strengthens confidence in our benchmark's ' ability to assess model performance in manner that reflects human perception.
A.4 MORE ANALYSIS WITH GENERATE ANY SCENE
With GENERATE ANY SCENE, we can generate infinitely diverse and highly controllable captions. Using GENERATE ANY SCENE, we conduct several analyses to provide insights into the performance of today's Text-to- Vision generation models.
A.4.1 PERFORMANCE ANALYSIS ACROSS CAPTION PROPERTIES
In this section, we delve into how model performance varies with respect to distinct properties of GENERATE ANY SCENE captions. While GENERATE ANY SCENE capable of generating an extensive diversity of captions, these outputs inherently differ in key characteristics that influence model evaluation. Specifically, we examine three properties the caption: Commonsense, Perplexity, and Scene Graph Complexity (captured as the number of elements in the captions). These properties are critical understanding how different models perform across a spectrum of linguistic and semantic challenges presented by captions with varying levels of coherence, plausibility, and compositional richness.
Perplexity. (Figure Perplexity is metric used to measure a language model's unpredictability uncertainty in generating text sequence. A higher perplexity value indicates that the sentences are less coherent or less likely to be generated by the model.
As shown Figure g From left to right, when perplexity increases, indicating that the sentences become less reasonable and less typical of those generated by language model, we observe no clear or consistent trends across all models and metrics. This suggests that the relationship between perplexity and model performance varies depending on the specific model and evaluation metric.
Commonsense. (Figure 9
Commonsense is an inherent property of text. We utilize the Vera Score (I),, metric generated by a fine-tuned LLM to evaluate text's commonsense level.
As shown in Figure D from left to right, as the Vera Score increases-indicating that the captions exhibit greater commonsense reasoning- we observe general improvement in performance across all metrics and models, except for Clip Score. This trend underscores the correlation between commonsense-rich captions and enhanced model performance.
Finally, we evaluate model performance across total element numbers in the captions, which represent the complexity of scene graphs (objects + attributes + relations).
Element Numbers (Complexity of Scene Graph). (Figure 10
From left right, the complexity of scene graphs becomes higher, reflecting more compositional and intricate captions. Across most metrics and models, we observe noticeable performance decline the scene graphs become complex. However, interesting exception is observed in the
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0.64
0.62
0.6
Average PICK Scare
Avernpe image Reseer
0.55
0.50
1.56
0.40
0.52
Perplexity Percentile
Perpiexity Percentils
Porplexity Percentile
(b)
Average Scare
0.35
VOA
TIRE
0.00
0.50
5.55
0.45
0.50
Perplexity Percentile
Perplesity Percentiie
Perplesity Percentlie
(f)
Models
Dalle-3
FLUXX11schhei
PixArt-2
Stable Diffusian v2-1 Stable Diffusien Medlum
SDXL
DeepFloyd XL
PixArt-a
Pyground v2.5
Wuerstchen v2
1-dev
Figure 8: Average performance of models across different percentiles of perplexity of captions, evaluated on various metrics. From left right, the perplexity decreases, indicating captions that are progressively more reasonable and easier for the LLM to generate.
0.64
Averggh Score
0.62
Average PICK Sooee
0.60
Average Imagee Resoard
8.6
0.36
0.45
0.54
0.2
04
0.6
15
0.2
0.40
322
0.4
0.6
0.
1.0
Vara Score Percentile
Vera Scorn Percentile
(a)
(b)
(c)
0.45
Titk 50me
0.40
Avsrage Score
VOA
0.45
0.25
0:20
0-
0.6
1.0
0.4
3.8
1.0
0.2
0.4
0.2
Vera Score Percentile
Vera Score Percentile
Vera Score Percentile
(d)
(e)
(f)
Models
Dalle-3
FLUX.1-schnell
PixAn--
Stable Diffusion v2-1
DeepFloyd
PixArt-a
Payground
Stable Diffusion Medium
Wierstchen
F-dev
Figure 9: Average performance of models across different percentiles of Vera Score for captions, evaluated various metrics. From left to right, the Vera Score decreases, indicating captions that exhibit less commonsense reasoning and are more likely to describe implausible scenes.
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0.54
0.02
Avgrage PICK Score
Average Image
Averog
0.54
Element
Element
Element
(b)
070
0.45
TIEN Score
Average Score
060
9.50
Avergge
0.30
2.50
0.25
020
12
0.35
Element
Element
Element
(d)
(c)
(f)
Models
Dalle-3
FLUX.1-schnel
PixArt-
Stable Diffusion v2-1
SDXL
DeepFloyd IF XL
PixArt-a
P ayground v2.5
Stable Diffusion 3 Medium
Wuerstchen v2
FLUX 1-dev
Figure 10: Average performance of models across different numbers of elements (objects + attributes + relations) in the scene graph (complexity of the scene graph) of the captions, evaluated on various metrics. From left right, the number of elements (complexity) increases, the scene graphs become more complicated and compositional.
performance of DaLL-E 3. Unlike other models, DaLL-E 3 performs exceptionally well VQA Score and TIFA Score, particularly on VQA Score, where it even shows a slight improvement as caption complexity increases. This suggests that DaLL-E's 3 may have unique capacity handle complex and compositional captions effectively.
A.4.2 ANALYSIS ON DIFFERENT METRICS
Compared with most LLM and VLM benchmarks that multiple-choice questions and accuracy metrics. There is no universal metric in evaluating Text-to- generation models. Researchers commonly used model-based metrics like Clip Score, VQA Score, etc. Each of these metrics created and fine-tuned for different purposes with bias. Therefore, we also analysis on different metrics.
Clip Score isn't universal metric. Clip Score is one of the most widely used metrics in Text- to- Vision generation for evaluating the alignment between visual content and text. However, our analysis reveals that Clip Score not perfect metric and displays
unusual trends. For instance, as shown in Figures 8 S] and [ we compute the perplexity across 10K captions used in our study, unpredictable disorganized text. Interestingly, unlike other metrics, Clip Score decreases as perplexity lowers, suggesting that Clip Score tends to favor more disorganized text. This behavior is counterintuitive and highlights the potential limitations of using Clip Score as a robust alignment metric.
where higher perplexity indicates
Limitations of human preference-based metrics. We use two metrics fine-tuned using human preference data: Pick Score and ImageReward Score. However, we found that these metrics exhibit strong bias toward the data which they were fine-tuned. For instance, shown in Table 5 Pick Score assigns similar scores across all models, failing to provide significant differentiation or meaningful insights into model performance. In contrast, ImageReward Score demonstrates clearer preferences, favoring models such as DaLL-E 3 and Playground v2.5, which incorporated human- alignment techniques during their training. However, this metric shows significant drawback:
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Performance Acress Different Genders
Performance Across Difierent Races
0.3
632
6.35
033
633
Figure 11: Average performance scores of all models across different genders evaluated using various metrics.
Figure 12: Average performance scores of all models across different races evaluated using various metrics.
it assigns disproportionately large negative scores models like SDv2.1, indicating potential over-sensitivity to alignment mismatches. Such behavior highlights the limitations of these metrics in providing fair and unbiased evaluations across diverse model architectures.
VQA Score and TIFA Score relative reliable metrics. Among the evaluated metrics, VQA Score and TIFA Score stand out by assessing model performance on VQA tasks, rather than relying solely subjective human preferences. This approach enhances the interpretability of the evaluation process. Additionally, we observed that the results from VQA Score and TIFA Score show stronger correlation with other established benchmarks. Based these advantages, we recommend prioritizing these two metrics for evaluation. However, it is important to note that their effectiveness is constrained by the limitations of the VQA models utilized the evaluation.
A.4.3 FATRNESS ANALYSIS
We evaluate fairness by examining the model's performance across different genders and races. Specifically, we calculate the average performance for each node and its associated child nodes within the taxonomy tree constructed for objects. For example, the node "females" includes child nodes such as "waitresses," and their combined performance is considered in the analysis.
Gender. In gender, we observe notable performance gap between females and males, as could be seen from Figure Models are better at generating male concepts.
Race. There are also performance gaps in different races. From Figure 12 we found that "white (person)" and "black (person)" perform better than "asian (person)". "Indian (amerindian)", and "Latin American".
A.4.4 CORRELATION OF GENERATE ANY SCENE WITH OTHER Text-to- Text-to-Vision generation BENCHMARKS
The GENERATE ANY SCENE benchmark uniquely relies entirely synthetic captions to evaluate models. To assess the transferability of these synthetic captions, we analyzed the consistency in model rankings across different benchmarks (79 37 122. Specifically, we identified the overlap of models evaluated by two benchmarks and computed the Spearman correlation coefficient between their rankings.
As shown the figure 13 GENERATE ANY SCENE demonstrates a strong correlation with other benchmarks, such as Conceptmix 779 and GenAl Bench (37), indicating the robustness and reliability of GENERATE ANY SCENE'S synthetic caption-bai based evaluations. This suggests that the synthetic captions generated by GENERATE ANY SCENE effectively reflect model performance trends, aligning closely with those observed in benchmarks using real-worte captions or alternative evaluation methods.
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Spearman Correlation Between Benchmarks
GensAl Bench HPSv2.1 Conceptmix Generate
1.00
0.94
0.50
1.00
0.9
0.94
1.00
1.00
0.80
0.5
0.50
1.00
1.00
0.50
0.7
-4
1.00
0.80
0.50
1.00
0.5
Generate Any Scene
Conceptmix
HPSv2 1 GenAl Bench
Figure 13: Correlation of GENERATE ANY SCENE with other popular Text-to-Vision generation benchmarks.
Weongg VVA Sooee
$00.
0.50
Desting
fonttoe
stoucter?
Atiiall
torgested
0.54
Aeusiig seouctart
mart Eoggententt
vebioe
Concept Canegery
Category
(a) SDv2./ vs. SD3 Medium on average VQ4 Score fine-grained categories.
(b) PixArt- vs. SD3 Medium on average VQA Score in mn--graned categorics.
Wecrge Score 0.5
8.48
Rec strel
Orblog
Bootore
Intontinn)
plot Inrgnsaat
Besting Intertane
Corpensoo
Category
Concegt Category
(c) FLUX. I-schmell vs. SD3 Medium average VQA Score in fine- gna-igai categories.
(d) PixAn-E vs. I-schelschnelll on average VQA Score in fine-grained categories.
Figure 14: Pairwise comparison on average VQA Score in fine-grained categories.
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A.4.5 CASE STUDY: PAIRWISE FINE GRAINED MODEL COMPARISON
Evaluating models using single numerical average score can be limiting, as different training data often lead models to excel in generating different types of concepts. By leveraging the taxonomy we developed for GENERATE ANY SCENE, we can systematically organize these concepts and evaluate each model's performance detailed comparison of how well models perform on individual concepts rather than relying solely on an overall average score. Our analysis revealed that, while the models may achieve similar average performance, their strengths and weaknesses vary significantly across different concepts. Here we present pairwise comparison of models across different metrics.
specific concepts over the taxonomy. This approach enables a more
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B DETAILS OF TAXONOMY OF VISUAL CONCEPTS
To construct a scene graph, we utilize three primary types of metadata: objects, attributes, and relations, which represent the structure of a visual scene. Additionally, scene attributes-which include factors like image style, perspective, and video time span-capture broader aspects of the visual content. Together, the scene graph and scene attributes form a comprehensive representation of the scene.
Our metadata is further organized using well-defined taxonomy, enhancing the ability to generate controllable captions. This hierarchical taxonomy not only facilitates the creation of diverse scene graphs, but also enables fine-gres fine-grained and systematic model evaluation.
Objects. To enhance the comprchensiveness and taxonomy of object data, we leverage noun synsets and the structure of WordNet (32). In WordNet, a physical object defined as "a tangible and visible entity; an entity that east shadow." " Following this definition, we designate the physical object as the root node, constructing a hierarchical tree with all 787 hyponyms under this category as the set of objects in model.
Following WordNet's hypernym-hyponym relationships, we establish a tree structure, linking each object to its primary parent node based its first-listed hypernym. For objects with multiple hypernyms, we retain only the primary parent to simplify the hierarchy. Furthermore, to reduce ambiguity, multiple senses of term share the same parent, we exclude that term itself and reassign its children to the original parent node. This approach yields a well-defined and disambiguated taxonomy.
Attributes. The attributes of scene graph represent properties or characteristics associated with each object. We classify these attributes into nine primary categories. For color, we aggregate 677 unique entries sourced from Wikipedia (33). The material category comprises 76 types, referenced from several public datasets 9 94 955. The texture category includes 42 kinds from the Describable Textures Dataset (66), while the architectural style encompasses 25 distinct styles (97). Additionally, we collect 85 states, 41 shapes, and 24 sizes. For human descriptors, we compile 59 terms across subcategories, including body type and height. Finally, we collect 465 common adjectives covering general characteristics of objects to enhance the descriptive richness of our scene graphs.
Relationships. We leverage the Robin dataset as the foundation for relationship metadata, encompassing six key categories: spatial, functional, interactional. social, emotional, and symbolic. With 10,492 relationships, the dataset provides a comprchensive and systematic repository that supports modeling diverse and complex object interactions. Its extensive coverage captures both tangible and abstract connections, forming a robust framework for accurate scene graph representation.
Scene Attributes. In Cext-to-Vision generation tasks, people mainly focus on creating realistic images and art from text description (8) 2 D. For artistic styles, we define scene attributes using 76 renowned artists, 41 genres, and 126 painting styles from WikiArt (9). along with 29 common painting techniques. For realistic imagery, we construct camera settings attributes across 6 categories: camera models, focal lengths, perspectives, apertures, depths of field, and shot scales. The camera models are sourced from the 1000 Cameras Dataset (100), while the remaining categories are constructed based on photography knowledge and common captions in Text-lo- Vision generation tasks (I [OT). To control scene settings. we categorize location, weather and lighting attributes, using 430 diverse locations from Places365 (35), alongside 76 weathers and 57 lighting conditions. For video generation, we introduce attributes that describe dynamic elements. These include 12 types camera rig, 30 distinct camera movements, 15 video editing styles, and 27 temporal spans. The comprehensive scene attributes that construct allow for the detailed and programmatic Text-to- Visiioo generation generation.
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C DETAILS OF SELF-IMPROVING MODELS WITH SYNTHETIC CAPTIONS
(SECTION 3)
C.1 EXPERIMENT DETAILS
C.I.1 CAPTIONS PREPARATION
To evaluate the effectiveness of our iterative self--mproving Text-to- -istionn generation model, generated three distinct sets of 10K captions using GENER ATE ANY SCENE, covering a sample complexity range from to 12. These captions programmatically created reflect spectrum of structured scene graph compositions, designed to challenge and enrich the model's learning capabilities.
For comparative analysis, we leveraged the Conceptual Captions (CC3M) (102) dataset, a large-scale benchmark containing approximately 3.3 million image-caption pairs sourced from web alt-text descriptions. CC3M is renowned for its diverse visual content and natural language expressions, encompassing a wide range of styles, contexts, and semantic nuances.
To ensure fair comparison, we randomly sampled three subsets of 10K captions from the CC3M dataset, matching the GENERATE ANY SCENF-generated caption sets in size. This approach stan- dardizes data volume while enabling direct performance evaluation. The diversity and semantic richness of the CC3M captions serve as a robust benchmark to assess whether GENERATE ANY SCENE-generated captions can match or exceed the descriptive quality of real-world data across varied visual contexts.
C.1.2 DATASET CONSTRUCTION AND SELECTION STRATEGIES
For the captions generated by GENERATE ANY SCENE, we employed a top-scoring selection strategy to construct the fine-tuning training dataset, using random selection strategy a bascline for comparison. Specifically, for each caption, the model generated eight images. Under the top-scoring strategy, we evaluated the generated images using the VQA score and selected the highest-scoring image as the best representation of the caption. This process yielded 10K top-ranked images per iteration, from which the top 25% (approximately 2.5k images) with the highest VQA scores were selected to form the fine-tuning dataset.
In the random selection strategy, one image was randomly chosen from the eight generated per caption, and 25% of these 10K randomly selected images were sampled to create the fine-tuning dataset, maintaining parity in data size.
For the CC3M dataset, each caption was uniquely paired with a real image. From the 10K real image-caption pairs sampled from CC3M, the top 25% with the highest VQA scores were selected as the fine-tuning training dataset. This ensured consistency in data size and selection criteria across all methods, facilitating rigorous and equitable comparison of fine-tuning strategies.
C.1.3 FINE-TUNING DETAILS
We fine-tuned the SDv1.5 using the LoRA technique. The training was conducted with a resolution of 512 x 512 for input images and a batch size of 8, Gradients were accumulated over two steps. The optimization process utilized the AdamW optimizer with 81 0.9, /33 0.999, an value of 1 x 10-8, and weight decay of 10-2 2 The learning rate was set to 1 x 10-4 and followed cosine scheduler for smooth decay during training. To ensure stability, a gradient clipping threshold of 1.0 was applied. The fine-tuning process was executed for one epoch, with maximum of 2500 training steps. For the LoRA-specific configurations, we set the rank of the low-rank adaptation layers and the scaling factor to be 128.
After completing fine-tuning for each epoch, we set the LoRA weight to 0.75 and integrate into SDv/.5 to guide image generation and selection for the next subset. For the CC3M dataset, images from the subsequent subset are directly selected.
In the following epoch, the fine-tuned LoRA parameters from the previous epoch are loaded and used to resumee training on the current subset, ensuring continuity and leveraging the incremental improvements from prior iterations.
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with our caption
N
Fine-tuned
There are four on the table, two pans very capa, the others do.
A black robot painting preffis wall.
A silver laptop open the foor,
browe squirrel holting two acorms treo.
bridge with no end. vantahing into the
boat with salls, adrift calm waterrs
There and two bananas which la bisger than the
Captions
with caption
Fine-tuned with oc
A street scone vintage bicycle parkest ked
A red rose in full rosebud garden..
A lorge feddy bear wearing beddy bear wearing party hal.
chooolate cuposke varilla vanilia supcako with trosting.
On the beach, two sand, adorned with bright
In an oahibiiion paintings, landecapen, and the painting the far
pir feeding group esger pond.
Captions
lamppost but not
plain with single
Figure 15: Visualization of Different Caption Fine-Tuning
In Figure 15 we present results using our captions and the CC3M captions. The model fine-tuned with captions generated by GENERATE ANY SCENE demonstrates superior performance in terms of text semantic relevance and the generation of complex compositional scenes.
C.2 EVALUATION TIFA BENCH
Aside from test set and GenAl benchmark, also evaluated fine-tuned Text-to-lmage generation models on the Tifa Bench (Figure [6]. where we observed the same trend: models fine-tuned with captions consistently outperformed the original SDv1.5 and CC3M fine-tuned models.
Average TIFA Score
78.5
Model
78.0
Original Model
CC3M-Fine-Tuned Model
77.5
Ours Random Selection Fine-Tuned Model
Ours Highest Scoring Fine Tuned Model
77.0
Oriqinal Model
76.5
Epoch
Epoch 2
Epoch 3
Finetune Epoch
Figure 16: Results for Application 1: Self-Improving Models. Average TIFA score of SDv1.5
fine-tuned with different data over TIFA Bench.
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C.3 ADDITIONAL BEAL-DATA BASELINES
Setup. We conduct more experiments comparing GENERATE ANY SCENE synthetic captions to other real-world caption sources. We sampled 10K captions from MS-COCO-2017 and LAION- COCO for one-epoch LoRA fine-tuning under same experimental settings. The results on GENERATE ANY SCENE test set are summarized in Table [T]
Table 11: Self-improvement on GENERATE ANY SCENE Test (VQA), One-epoch finetuning, equal budget.
Method
VQA
Baseline (SDvI.5)
0.508
MS-COCO-2017
0,508
LAION-COCO
0,510
CC3M
0,508
GAS (Random)
0.524
GAS (Top-Score)
0.530
Findings. Fine-tuning with MS-COCO-2017 and LAION-COCO captions yields results similar CC3M, with none surpassing the significant improvements achieved by our GENERATE ANY SCENE captions. We think that although MS-COCO-2017 and LAION captions are generally high-quality and well-aligned with images, they offer limited compositional diversity. These additional results confirm that the observed gains are not specific CC3M but generalize across other widely used real-caption datasets. This further supports claim that the compositional diversity of GENERATE ANY SCENE synthetic captions drives the improvement.
C.4 FULL TUNI-G VS. LORA FINE-TUNING
Setup. replicate the self- seff-improvement pipeline with fill fine-tuning and compare three strategies: GENERATE ANY SCENE captions with high-score selection, GENERATE ANY SCENE captions with random selection, and CC3M captions the real-data baseline. The results shown in Tables 12 and B
Table 12: Results on GENERATE ANY SCENE test set under full fine- toningunn...... (VQA Score)
Method
Iter-1
Iter-2
Iter-3
Baseline
0.508
CC3M (Full FT)
0.496
0.518
0.519
GAS (Rand, Full FT)
0.510
0,519
0.520
GAS (Top, Full FT)
0.510
0.534
0.540
Table Results on GenAl-Bent under full fine-tuning, (VQA Score)
Method
Iter-
Iter-2
Iter-3
Baseline
0.617
CC3M (Full FT)
0.589
0.619
0.622
GAS (Rand, Full FT)
0.599
0.621
0.617
GAS (Top, Full FT)
0.620
0.626
0.634
Findings. Using GENERATE ANY SCENE captions with high score selection not only improves performance consistently across iterations but also surpasses CC3M at every stage. The full fine- tuning results confirm that captions and strategy's effectiveness is not dependent the specific training approach (LoRA vs. full fine-tuning). The consistent improvement patterns across both evaluation benchmarks validate the robustness of our iterative seff-improvement framework.
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D DETAILS OF DISTILLING TARGETED CAPABILITIES (SECTION 4)
D.1 COLLECTING HARD CONCEPTS
We evaluate both models on 10K GENERATE ANY SCENE captions and select challenging object concepts where SDv1.5 and DaLL-E 3 exhibit the largest gap. To determine the score for each concept, we calculated the average TIFA Score of the captions containing that specific concept. For each targeted-generraeed caption, we generate four images and use the one with the highest VQA Score. The full list of hard concepts shown below:
1. cloverleaf
2. aeric (habitation)
3. admixture
4. webbing (web)
5. platter
6. voussoir
7. hearthstone
8. puttee
9. biretta
10. yarmulke
11. surplice
12. overcoat
13. needlepoint
14. headshot
15. photomicrograph
16. lavaliere
17. crepe
18. tureen
19. bale
20. jetliner
21. square-rigger
22. supertanker
23. pocketcomb
24. filament (wire)
25. inverter
26. denture
27. lidar
28. volumeter
29. colonoscope
30. synchrocyclotron
31. miller (shaper)
32. alternator
33. dicer
34. trundle
35. paddle (blade)
36. harmonica
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37. piccolo
38. handrest
39. rundle
40. blowtorch
41. volleyball
42. tile (man)
43. shuttlecock
44. jigsaw
45. roaster (pan)
46.
47. belt (ammunition)
48. gaddi
49. drawer (container)
50. tenter
51. pinnacle (stceple)
52. pegboard
53. afterdeck
54. scaffold
55. catheter
56. broomcomn
57. spearmint
58. okra (herb)
59. goatsfoot
60. peperomia
61. ammobium
62. gazania
63. echinocactus
64. birthwort
65. love-in-a-mist (passionflower)
66. ragwort
67. spicebush (allspice)
68. leadplant
69. barberry
70. hamelia
71. jimsonweed
72. undershrub
73. dogwood
74. butternut (walnut)
75. bayberry (tree)
76. lodestar
77. tapa (bark)
78. epicalyx
79. blackberry (berry)
80. stub
81. shag (tangle)
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D.2 EXPERIMENT DETAILS
We conducted targeted fine-tuning experiments on SDv1.5 to evaluate GENERATE ANY SCENE'S effectiveness in distilling model compositionality and learning hard concepts. For each task, we selected a dataset of 778 GENERATE ANY SCENE captions paired with images generated by DaLL- 3. For compositionality, we selected multi-object captions from the existing dataset of 10K GENERATE ANY SCENE captions and paired them with the corresponding images generated by DaLL-E 3. To address hard concept learning, we first used SDv1.5 to generate images based on the 10K GENERATE ANY SCENE captions and identified the hard concepts with the lowest VQA scores. These concepts were then used to create a subset of objects, which we recombined into our scene-graph based captions with complexity levels ranging from 3 to 9. Finally, we used DaLL-E 3 to generate corresponding images for these newly composed captions.
The fine-tuning configurations were consistent with those used in the self-improving setup (Ap- pendix C.1.3). To accommodate the reduced dataset size, the maximum training steps were set to 1000.
As a baseline, we randomly selected 778 images from 10K GENERATE ANY SCENE- -eene-ggeeeated images, using captions produced by GENER ANY SCENE, This ensured controlled comparison between the targeted and random fine-tuning strategies.
D.3 BENCHMARK AGAINST WEB-CRAWLED CAPTION-IMAGE PAIRS
Setup. We conduct additional experiments to benchmark against alternative data sourcing strategies, specifically comparing our DaL.L-E distillation approach with web-scraped real images. Using the Bing Image Search API, we retrieve images matching our multi-object and hard-concept captions and constructed two datasets of equivalent scale for comparison. then apply the same fine-tuning setup described in Application 2. The results are shown in Table 14
Table 14: Comparison of VQA scores from targeted fine-tuning on different data sources. (SDvI.5)
Test Set
Original
Dal.I-E3 3 Distill
Web-crawled
Hard Concept
0.303
0.361 0.325
0.258 0.264
Multi-object
0.271
Findings. The results show that web-scraped images not only failed improve performance but actually degraded model capabilities.
Upon examination the retrieved images, we identify several critical issues. The web-crawled images contain significant noise, including watermarks, overlaid text, and irrelevant visual element. Our hard concept and multi-object captions feature high compositional complexity and novel object combinations that rarely exist in real-world photographs. The retrieved images show poor relevance to our systematically designed compositional scenarios, as real-world images cannot adequately represent the diverse and controlled compositional variations we programmatically generate. Thus, training on such misaligned data appears to introduce incorrect visual-tent texuutl associations, leading to performance degradation rather than improvement.
Table 15: VQA Score of targeted distillation on FLUX. I-dev.
Test Set
Original
Fine-tuned
Hard Concept
0.303
0.361 0.325
Multi-object
0.271
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D.4 DISTILLATION ON FLUX.I-DEL
Setup. We further apply our distillation framework to FLUX. a current SOTA open-source model, using DaLL-E 3 -generated images of hard concepts and multi-object captions to distill these capabilities into FLUX.I-dew The results are shown in the Table 15
Findings. The results demonstrate that our approach's effectiveness extends state-of- tfat-of--the-at -hetttr models (FLUX. 1-dev). The distillation approach yields substantial improvements challenging compositional tasks.
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E DETAILS OF REINFORCEMENT LEARNING WITH A SYNTHETIC REWARD FUNCTION (SECTION 5)
E.I TRAINING DATA PREPARATION
We adopt SimpleAR-O.. -..5----FFFF SFT (66 as our base model. Given that SimpleAR-0.5B. is pretrained on high-quality real image datasets such as LAION (II) and CC3M (12) aim mitigate potential distributional shift between the original training data and the reinforcement learning phase. To this end, we perform metadata pre-selection for GENERATE ANY SCENE by analyzing the frequency of each object category appearing in the LAION dataset. Leveraging the controllable compositional capabilities of GENERATE ANY SCENE, filter object categories by selecting the top 10% most frequent entries and constrain scene complexity to 3-6 objects per scene. Based on these conditions. we synthesize set of 10K captions, ensuring semantic alignment with the base model's pretraining distribution while maintaining structural and content diversity.
E.2 EXPERIMENT DETAILS
The detailed training configuration is provided in Table T6 We utilize NVIDIA H100 GPUs (80GB HBM3), with one GPU allocated for online generation using vLLM. The total training time is approximately hours.
Table 16: Scene-gran graeegrrpphhhhh based GRPO Fine-tuning Configuration for SimpleAR
Component
Details
Model Name
SimpleAR-0.5B-SFT
Model Size
~0.5B parameters
Training Policy
GRPO
Inference Engine
vLLM (GPU utilization = 0.7)
Completion Length
4096 tokens
Training Epochs
Batch Size per Device
4
Learning Rate
1
10-5
Scheduler
Cosine Annealing (min Ir rate = 0.1)
Warm-up Ratio
0.1
Gradient Accumulation
1
0.5
0.4
Reward
0.3
0.2
Raw Reward
0.1
Smoothed Reward
0
200
400
600
800
1000
1200
1400
Global Step
Figure 17: Reward progression during scene-graph based GRPO training.
Figure 17 illustrates the reward progression during training. A noticeable improvement in reward observed following the application of learning rate of le-5 combined with warm-up strategy.
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Overall, the reward increases by approximately 0.2, indicating effective learning under the adjusted training configuration.
In 4 we observe that the reproduced results of baseline models DPG-Bench and GenEval Bench are slightly lower than those reported in the original paper. Considering the inherent stochas- ticity in generative model outputs, cite the original results for comparison. For GenAl-Bench, all reported results are based on our own experimental evaluations.
E.3 REWARD VARIANTS AND ABLATIONS
Setup. To verify the observed gains arise specifically from the scene geene-graph-ae gyrahh-generatee QA reward, rather than simply from using any QA-based reward, we conduct experiments incorporating manually annotated QA datasets, VQAv2, as additional reward signals under the same RLHF framework. We sample 10K images from VQAv2, with corresponding QA pairs, matched them to COC02017 captions, and apply same training frameworks to SimplcAR-0.58 -.55B----- with RL training. The results on GenAl Bench are shown in the table:
Table 17: GenAl Bench performance (VQA) under RLHF with different reward sources. All models start from SimpleAR-O.5B-SFT
Method
Basic f
Advanced t
All
SimpleAR-O.5B-SFT
0.74
0.60
0.66
SimpleAR-0.5BB- (CLIP)
0.75
0.60
0.67
GimpleAR-0.5B-R (VQAv2)
0.73
0.59
0.66
SimplcAR-0.5B- S-RLLLLLAA (Ours)
0.75
0.61
0.68
Findings. The results show that using VQAv2 captions and QA pairs rewards yields lower performance than CLIIP-based RI training. Furthermore, we observe minimal reward improvement from VQA signals throughout training. We attribute this to the fact that, although VQAv2 QA pairs are rich, the underlying image captions fail to cover enough visual elements, leading to a mismatch between QA pairs and captions that undermines RLHF reward alignment.
This highlights the inherent difficulty and cost of constructing high-quality image- caagee- and QA annotations, whereas our method leverages cene-graph structures to systematically generate synthetic caption- QA pairs at minimal cost with unique advantages.
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F DETAILS OF IMPROVING GENERATED-CONTENT DETECTION (SECTION 6)
F.1 EXPERIMENT DETAILS
In this section, our goal is to validate that the more diverse captions generated by GENERATE ANY SCENE can complement existing datasets, which are predominantly composed of images paired with captions. By doing so, we aim to train Al-generated content detectors to achieve greater robustness.
Dataset preparation We conducted comparative experiments between captions generated by GENERATE ANY SCENE and entries from the DS dataset. From the D dataset, we randomly sampled 10K entries, each including caption, link to a real image, and an image generated by SD v1.4. Due to some broken links, we successfully downloaded 8.5K real images and retained 10K SD vl.4-generated images. We also used SD v1.4 to generate images based on 10K GENERATE ANY SCENE captions.
We varied the training data sizes based on the sampled dataset. Specifically, we sampled N real images from the 10K D real images. For synthetic data, we compared N samples exclusively from D with a mixed set of N/2 samples from 10K GENERATE ANY SCENE images and N/2 sampled from D3 ensuring total of N synthetic samples. Combined, this resulted in 2N training images. We tested 2N across various sizes, ranging from 2K to 10K.
Detector architecture and training We employed VIT-T 47 and ResNet-18 (105) as backbones for the detection models. Their pretrained parameters ImageNet-21K were frozen, and the final classification head was replaced with a linear layer using a sigmoid activation function to predict the probability of image being Al- Al-generated. During training, We used Binary Cross- Cross-Entroppy (BCE) as the loss function, and the A optimizer was applied with learning rate of 2e 33 Training was conducted with batch size of 256 for up 50 epochs, with early stopping triggered after six epochs of no improvement in validation performance.
Testing To evaluate the performance of models trained with varying dataset sizes and synthetic data combinations, we tested them on both Genlmage and GENERATE. ANY SCENE datasets to assess their in-domain and out-of-domain performance under different settings.
For Genlmage, used validation data from four models: SD v1.4, SD v1.5, MidJoumey, and VQDM. Each validation set contained 8K real images and 8k generated images. For GENERATE ANY SCENE, we sampled 10K real images from CC3M and paired them with 10K generated images from each of the following models: SDv2.1, PixAri-a, SD3 Medium, and Playground v2.5. This created distinct test sets for evaluating model performance across different synthetic data sources.
Table 18: Fl-Score Comparison of ResNet-18 and ViT-T Detectors Trained with D and D GENERATE ANY SCENE Across In- In-Domain Settings
Detector
(2N)
[Roddumoin,
SDr14
SUxI-medium
Playground
(Inddminn,
Average
017822
0.6724
Reser-18
0.2886
0.3493
0.6828
0,2700
0.3454
10K
IU6957
0.336
0.629
0,6755
07550
0.7533
074775
01641
0.6878
0.6711
0.7576
0.7625
0.7071
0.754
0.7217
0.514
0.6558
VIT.T
0,6898
0.7663
0.7656
0.7629
0.5134
0.6875
0.6974
0.7655
0.7712
0.7655
0.7253
0.6344
0.6936
10K
0.6966
0.6984
0.7828
0,7777
0.7275
0.
0.7334
0.633
0.7680
0
F.2 RESULTS
Table 19 and Table 18 evaluate the performance of ResNet-18 and VIT-T detection backbones trained on datasets of varying sizes and compositions across in-domain (same model and cross- mooss) and out-of-domain settings. While models trained with D and GENERATE ANY SCENE occasionally underperform compared to those trained solely on D in the in-domain same-model setting, they exhibit significant advantages in both in-domain cross-model and out-of-domain evaluations. These
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results demonstrate that incorporating data (GENERATE ANY SCENE) into the training process enhances the detector's s robustness. By supplementing existing datasets with GENERATE ANY SCENE under the same training configurations and dataset sizes, detectors achieve stronger cross model and cross-dataset capabilities, highlighting improved generalizability to diverse generative models and datasets.
Table 19: FI-Score Comparison of ResNet- 18 and ViT-T Detectors Trained with D and D GENERATE ANY SCENE Across Out-of-Domaii Settings
Average
Data Scale (2N)
SDv1.5
VQDM
Midjourney
Detector
(Out-of-domain)
D + Ours
D + Ours
D
Ours
D
D + Ours
2K
0.6515
0.6591
0,5629
0.5285
0.5803
0,5647
0.5982
0.5841
0.6709
0.6817
0.5693
0.5428
0.6016
0.5941
0.6139
0.6062
Resnet-18
0,6750
0.6963
0.5724
0.5327
0.6084
0.6072
0.6186
0.6121
0.6792
0.6965
0.5716
0.5282
0.6097
0.5873
0.6202
0.6040
10K
0.6814
0.6955
0,5812
0.5454
0.6109
0.6040
0.6245
0.6150
2K
0.6755
0.6685
0.5443
0.4966
0.6207
0.6066
0.6135
0.5906
4K
0.6845
0.6865
0.5591
0.4971
0.6416
0.6149
0.6284
0.5995
VIT-T
6K
0.6900
0.6890
0.5580
0.4948
0.6455
0.6259
0.6313
0,6032
8K
0.6940
0.6969
0.5553
0.4962
0.6495
0.6387
0.6329
0.6106
10K
0.6961
0.6988
0,5499
0.4975
0.6447
0.6358
0.6302
0.6107
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G ADVANTAGES OF GENERATE ANY SCENE OVER LLM-DRIVEN SCENE GRAPH AND CAPTION GENERATION
GENERATE ANY SCENE is conceptually superior well-prompped LLM for large-scale scene graph and corresponding captions generation. While modern LLMs are powerful, they do not provide the guarantees required for systematic, controllable, and reproducible enumeration of compositional structures. In contrast, GENERATE ANY SCENE explicitly enumerates graph topologies under user- specified constraints (e.g.. complexity, topics, connectivity) and then deterministically instantiates them, yielding uniform coverage, strict structural validity, and high efficiency.
Controllability and Diversity. GENERATE ANY SCENE explicitly enumerates scene graph struc- tures and populates with user-specified configuration and taxonomy (e.g., complexity, topics, connec- tivity, etc.), ensuring systematic coverage of rare or unconventional compositions without requiring users manually write prompts for desired structures. In contrast, an LLM tends to default to common patterns in its training distribution. For example, given only the metadata (book, table, on), an LLM will prefer the statistically dominant configuration "the book on the table", and struggle to produce the less common but equally valid "the table is on the book" without extensive prompt engineering. Moreover, such extensive or high-quality prompting for scene graph generation essentially requires the user to manually enumerate graph structures and design multiple templates in natural language, whereas GAS accomplishes this systematically with a single program.
Reduced Bias and Hallucination. Relying on LLMS to generate large-scale captions inherently inherits their internal biases and increases the likelihood of hallucinating unseen or semantically inconsistent object configurations. GENERATE ANY SCENE avoids this by enumerating scene graphs and then deterministically mapping them to captions, producing text that is faithful by construction to the underlying graph structure.
Lower Cost and Higher Reproducibility. In GENERATE ANY SCENE, once a scene graph is enumerated, it is cached and reused across multiple populations, and it can also serve as seed graph for controllable topological expansion without re-enumerating the entire structure. Combined with our fully programmatic operations, this makes large-scale generation substantially more cost-efficient. In contrast, relying on an LLM would require repeated API calls or prompt redesign for structural variant and new content, making the process both costly and labor-intensive.
To empirically validate these points, we compare GENERATE ANY SCENE against Gemini 2.5- flash on generating 10K scene graphs from our common metadata (3,649 items: 2,591 objects, 551 attributes, 507 relations). Because Gemini becomes increasingly error-prona when prompted with the full metadata list, we adopt batching strategy: in each batch we randomly sample 5% of the metadata 182 items) and prompt the model to generate 20 scene graphs containing 3-12 12 elements.
Table 20 shows the distribution quality and diversity of generated elements. GENERATE ANY SCENE achieves near-uniform usage across objects, attributes, and relations, with Gini coefficients between 0.14 and 0.17 and normalized entropy above 99.3%. Gemini, in contrast, exhibits strong concentration (Gini 0.53-0.66) and substantially lower entropy (79.5-92.5%). indicating a tendency to overuse a narrow subset of frequent categories. The top- 10% coverage further highlights this imbalance: under Gemini, 37. 37.29% of object occurrences and 50.38% of relation occurrences are concentrated in only 10% of the vocabulary, whereas GENERATE ANY SCENE remains close to the uniform ideal.
Beyond distributional properties, we assess structural validity and data quality using strict schema- level checks (Table 21). GENERATE ANY SCENE produces 100% structurally valid graphs with zero hallucinated elements. In contrast, only 49. 49.1% of Gemini's outputs satisfy the schema. Common failure modes include treating relations as nodes (34.6% of graphs), and omitting required va ue (31.2%) or type (30.3%) fields. Gemini also hallucinates 1,773 "unknown" objects (4.59% of all objects) and 3,638 "unknown" relations.
Finally, GENERATE ANY SCENE is more efficient than LLM-based generation (Table 22
Because
GENERATE ANY SCENE uses programmatic enumeration, it generates 10K scene graphs in under one minute, with negligible cost. In contrast, Gemini requires 1.5 hours and incurs over $50 of API cost for the same workload. Overall, GENERATE ANY SCENE provides a 90 speedup and near- zero marginal expense.
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Table 20: Distribution quality and diversity of generated scene graphs.
Metric
GAS (Ours)
Gemini 2.5-flash
Gini Coefficient (U)
Objects
0,14
0.53
Attributes
0.14
0.57
Relations
0.17
0.66
Normalized Entropy (1)
Objects 99.6%
92.5%
Attributes 99.3%
91.7%
Relations
99.3%
79.5%
Top 10% Coverage (L)
Objects 14.68%
37.29% 50.38%
Relations 15.41%
Table 21: Structural validity and data quality of generated scene graphs.
Metric
GAS (Ours)
Gemini 2.5-flash
Structurally valid graphs
100%
49.1%
Graphs with relations as nodes (error)
0%
34.6%.
Graphs missing vall value iee field
0%
31.2%
Graphs missing type field
0%
30.3%
Hallucinated "unknown" objects
0
1,773 (4.59%)
Hallucinated "unknown" relations
0
3,638
These results confirm that programmatic enumeration in GENERATE ANY SCENE outperforms LL.M-based generation, providing the systematic guarantees of uniformity, validity, and efficiency.
H DISCUSSION
H.1 COMMONSENSE AND PLAUSIBILITY FILTERING
GENERATE ANY SCENE enables systematic, controllable, and diverse compositional scene construc- tion through programmatic scene graph enumeration. This allows the synthesized captions to cover only realistic scenes commonly observed in the real world, but also uncommon, imaginative, and unrealistic scenes. Many widely-used generative models, including DALL-E, Midjourney, and Sora/Sora2, derive much of their practical value from producing surreal, imaginative, or physically unlikely compositions (e.g.. "an astronaut riding a horse the moon," or "a raccoon astronaut with glowing space donut"). Such prompts are not outliers; they reflect common user intents art, game design, advertising, and entertainment. Users frequently employ abstract fantastical combinations precisely to explore the model's creativity, and the community often discusses and evaluates models based on performance systematically controlled compositional space essential for improving and benchmarking modern generative models. Limiting sampling only strictly "realistic" combinations would substantially reduce both the training and the evaluation value.
these highly "unrealistic" prompts. From research perspective, broad and
Table 22: Efficiency and cost of generating 10K scene graphs.
Metric
GAS (Ours)
Gemini 2.5-flash
Generation time (10K graphs)
< minute
1.5 hours
Monetary cost
Negligible
$50
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Our approach is specifically designed to meet this need for diverse captions and systematic visual representations. At the same time, GENERATE ANY SCENE differentiates uncommon or unrealistic scenes from nonsensical scenes. The taxonomy enforces strong type-level constraints, e.g,, architec- tural attributes apply only to buildings, human-specific attributes only to the "person" " subtree, and attentional relations only between animate entities, ensuring that generated scenes remain meaningful and structurally valid, even when creatively unrealistic. Beyond these inherent structural constraints, GENERATE ANY SCENE additionally provides an optional two-stage commonsense and plausibility filtering mechanism to support use cases that require higher visual realism. (1) Pre-population filtering. We maintain for every object/atrrbbute/rlatton its LAION-5B ( frequeney and em- bedding representation, and select candidates by jointly enforcing minimum frequency thresholds and semantic coherence: for each newly added relation or attribute attached to a given object, we compute the top-k semantically compatible candidates based on embedding similarity to that object. Likewise, when expanding a relation triple, we compute candidate object similarity to the anchor object within the triplet, including all attributes and relations already attached to the anchor, and then sample from the top-k most semantically compatible objects (where k is user-configurable) Users may further specify complexity limits to avoid highly complex scenes. (2) fost-population filtering. After population, once the scene graph is translated into caption, we compute its Vera score (9 and caption perplexity, and discard captions falling below plausibility or above perplexity thresholds. These mechanisms ensure that GENERATE ANY SCENE preserves meaningfulness while still enabling broad creative coverage.
H.2 SOCIAL BIAS
Assessing social bias is important for understanding whether synthetic data introduces unintended shifts model behavior. To examine this, we evaluate models on gender-related prompts from the DALL-Eval (144) benchmark, comparing SDv1.5, 1.5 fine-tuned on CC3M captions, and SDv1.5 fine-tuned on GENERATE ANY SCENE captions. The gender MAD results are shown in Table 23 The experiment shows that fine-tuning with GENER ANY SCENE does not amplify gender bias relative to the base model. We attribute this to our design choices. First, GENERATE ANY SCENE does not generate data by propagating textual descriptions or cultural associations from these sources; instead, our metadata is used purely as a structural vocabulary of objects, attributes, and relations. GENERATE ANY SCENE doesn't 't sample linguistic definitions or corpus-derived stereotypes from WordNet. Second, the systematic, programmatic nature of our scene scene-graph enumeration further reduces the pathways through which social bias present in real-world distributions could propagate. Also, any more debiased metadata can be plugged into GENERATE ANY SCENE engine seamlessly.
Table 23: Gender MAD Scores on DALLEval
Model
MAD
SDv1.5
0,3602 0,3476
FT w/ CC3M
FT w/ GAS
0.3555
I LIMITATION
Programmatically generated prompts can be unrealistic and biased. Programmatically gener- ated prompts can be unrealistic and biased. Although our system is capable of producing a wide range of rare compositional scenes and corresponding prompts, some of these outputs may violate rules or conventions, going beyond what even considered imaginable or plausible. We also implement a pipeline to filter the commonsense of the generated prompts using the Vera score (a large language model-based commonsense metric) and Perplexity, but we make this pipeline optional.
Linguistic diversity of programmatic prompts is limited. While GENERATE ANY SCENE excels at generating diverse and compositional scene graphs and prompts, its ability to produce varied language expressions is somewhat constrained. The programmatic approach to generating content ensures diversity in terms of the elements of the scene, but is limited when it comes to linguistic diversity and the richness of expression. To address this, we introduce pipeline that leverages large
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language models (LLMs) to paraphrase prompts, enhancing linguistic variety. However, this addition introduces new challenges. LLMs are prone to biases and hallucinations, which can affect the quality and reliability of the output. Furthermore, the use of LLMs risks distorting the integrity of the original scene graph structure, compromising the coherence and accuracy of the generated content. So we make this LLM paraphrase pipeline optional for our paper.
Toward curriculum-aware GRPO training. Our proposed GENERATE ANY SCENE framework plays a central role GRPO training by providing structured scene graphs that serve as the foun- dation for semantically grounded and controllable reward function. This design enables effective optimization by aligning generation objectives with fine-grained visual semantics. Beyond this, we also observe that GENERATE ANY SCENE also offers broader potential: the scene graphs it produces vary in complexity, such as the number objects, attributes, relationships and graph degree. These variations naturally correspond different levels of generation difficulty and reward variance. This property suggests an opportunity for curriculum- based training, where the model could be progressively exposed to increasingly complex scene graphs. Such a strategy may improve training stability and efficiency, especially in the early stages of learning. We identify this as promising direction for future work, further leveraging the controllability of GENERATE ANY SCENE to guide structured policy learning.
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Reviews:
[{"review_id": "1i6uyXD2r5", "reviewer": "Reviewer_kfjq", "summary": "This paper introduces “Generate Any Scene”, a data generation pipeline to sample scene graphs from a set of ~29k objects, ~1.5k attributes, ~10k relations, and ~2k scenes. Generate Any Scene also generates corresponding captions and QA pairs from sampled scene graphs in a programmatic way. In the paper, the authors incorporate the approach into a model self-improvement pipeline where graphs and captions are first sampled, and images / videos are then synthetically generated from those captions (using SDv1.5 for images and Text2Video-Zero for video). This synthesized data is then used for LoRA fine-tuning. The authors compare their approach to CC3M as a baseline and evaluate on text-to-vision generation benchmarks including GenAI-Bench. The authors also experiment with incorporating the proprietary DaLL-E 3 model into the pipeline as a more powerful model to distill from. Additionally, the paper experiments with GRPO in an RL context to fine-tune SimpleAR-0.5B-SFT using captions generated by the pipeline, and Qwen2.5-VL-3B as the reward evaluator. Finally, the paper explores the usage of Generate Any Scene in the context of content moderation.", "questions": "The paper mentions the usage of commonsense plausibility filtering to prune implausible contents while retaining compositional diversity. Can the authors elaborate on how this works in detail?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper presents a useful data generation pipeline based on scene graphs that can be incorporated into model self-improvement pipelines for image and video generation. The paper provides extensive experimental results and the proposed approach is technically sound. Given the reported results this approach can be useful for researchers working across this field.", "weaknesses": "My main concerns with this paper are as follows:\n* Technical novelty: the paper contributes very little technical novelty as its main contribution is the provision of components for scene graph sampling. It would have been interesting to see (at least a discussion of) different, more advanced sampling methods from the data repository to further improve downstream task performance.\n* Limitations: the paper does not seem to discuss limitations of their proposed approach. It would be great if additional limitations and directions for future work could be discussed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9RkwsK1Qu1", "reviewer": "Reviewer_Lusx", "summary": "This paper introduces \"Generate Any Scene\", a data engine that systematically generates diverse and compositional scene graphs from a large taxonomy of objects, attributes, and relations. These scene graphs are translated into textual captions for text-to-image/video/3D generation and into exhaustive visual question-answering (VQA) pairs for automatic evaluation. The authors demonstrate the system's utility across four applications: iterative self-improvement of generative models, targeted capability distillation from proprietary to open-source models, reinforcement learning with a scene-graph-based reward function, and enhanced AI-generated content detection. The core claim is that this structured, programmatic approach to synthetic data generation overcomes the limitations of noisy, weakly compositional web-crawled datasets, leading to significant improvements in model performance and semantic alignment.", "questions": "please see weaknesses", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper identifies a clear and significant problem in text-to-vision generation: the lack of structured, compositionally rich training data. The proposed solution using a programmatic scene graph engine to systematically enumerate the visual space is well-motivated.\n\n2. The paper provides extensive experimental validation across multiple tasks (text-to-image, video, 3D), models, and benchmarks (DPG-Bench, GenEval, GenAI-Bench). \n\n3. A significant strength is the demonstration of the system's versatility across four distinct applications (self-improvement, distillation, RLHF, content moderation). This shows the proposed engine is a general-purpose tool for advancing the field.\n\n4. The programmatic generation of both captions and exhaustive QA pairs for evaluation/reward modeling is highly scalable and avoids the costs and biases associated with LLMs or manual annotation.", "weaknesses": "1. I am concerned that randomly sampling metadata (objects, attributes, relations) may result in implausible or nonsensical scenes (e.g., \"crispy dog holding a rabbit reciting a poem\"). I am not sure if he \"commonsense plausibility filtering\" mentioned in Section 2.1 addresses this problem, it is not clearly described, making it difficult to assess its effectiveness. For scalable, fully automated operation, this remains a potential source of noise and inefficiency, as the T2I model and evaluator would waste resources on unrealistic prompts.\n\n2. The method's effectiveness, particularly in the self-improvement loop, is heavily dependent on the initial T2I model's generation efficiency and is computationally expensive. It requires generating multiple images per caption (e.g., 8 in the paper) and running VQA evaluation on all of them to select the best. This process of generation, storage, and evaluation consumes significant resources and time, which may limit its practicality.\n\n3. The process for identifying \"hard concepts\" for distillation (Section 4) is a great idea, but its description is somewhat vague. A more detailed explanation of how concepts are selected from the taxonomy and how the performance gap is quantified would strengthen this part of the methodology.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "g2OTJXzw8i", "reviewer": "Reviewer_wQKs", "summary": "This paper proposes a data engine that can generate scene graphs based on libraries of objects, attributes and relations. The proposed engine also generates detailed captions and visual question-answer pairs for each scene graph. The paper further introduces several applications of the proposed data engine, including self-improving text-to-image generation, model capability transfer, reinforcement finetuning, and content moderation. Experimental results show that proposed engine can improve performance of baseline models.", "questions": "See above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper is well written and easy to follow. Multiple tasks are tackled with the proposed approach, demonstrating the broad applicability.\n\n2. Extensive experiments are performed, trying to support the claims of the paper.", "weaknesses": "1. It is understandable that the proposed data engine can generate faithfully compositional captions. However, it is not convincing that such an engine can improve the compositional generation performance of a text-to-image generative model. Once a text-to-image generative model is trained, the distribution is there. When data engine and VQA score are used to select self-generated images and finetune the model, it is just biasing the model to overfit region of high VQA score. Then of course, the finetuned model will have better VQA score than baseline model. Is there any other metric that can demonstrate compositionality? Will such overfitting destroy the diversity of generated samples? How about the sample quality of the finetuned model? In other words, are you trading generation quality for VQA score? \n\n2. If the approach faithfully improves compositionality, then generalization performance would be enhanced at the same time. For example, the finetuning data contains combinations of a set of objects and attributes, the model can generate samples containing unseen combination of the same set of objects and attributes. Is this the case?\n\n3. Is there any reason why some tasks use TIFA score, while others use VQA score?\n\n4. In the main text, it mentions multiple times that more details are provided in the Appendix, which, however, is not attached. Especially, Section 7 give some conclusions, but no results are provided.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "npliRGqDDo", "reviewer": "Reviewer_oFpL", "summary": "This paper tackles poor compositional generalization in text-to-vision models using a data engine called GENERATE ANY SCENE. The engine systematically enumerates complex scene graphs from a rich taxonomy and translates them into captions for training and VQA pairs for reward modeling. This synthetic data enables model self-improvement, targeted distillation of proprietary model abilities , and low-cost reward functions.", "questions": "1. Could you elaborate on the implementation mechanism of the \"commonsense plausibility filtering\" in Step 1? Is it rule-based or model-based?\n2. In the iterative self-improving framework in Section 3, the model learns from its own high-scoring outputs. After multiple rounds of iteration, is there a risk of mode collapse or a reduction in diversity?\n3. Regarding the capability distillation in Section 4, if DALL-E 3 also fails to correctly generate a complex synthetic caption, is this \"failed\" sample still valuable for fine-tuning SDv1.5?\n4. Given the system's reliance on metadata sources like WordNet, have the authors analyzed the risk of amplifying inherent social biases in the synthetic data?\n5. Why is your programmatic enumeration approach fundamentally superior to using a well-prompted SOTA LLM to generate both the scene graphs and the diverse captions?\n6. Why is your \"flat,\" systematically enumerated data distribution superior to the \"natural\" distribution of real-world data, and does it risk harming model performance on common, real-world prompts?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper demonstrates originality by proposing a programmatic data engine, GENERATE ANY SCENE, for systematically synthesizing scene graphs and translating them into captions and accompanying VQA pairs. This method of tightly coupling data generation (captions) with automated evaluation (QA pairs) is novel, bypassing the reliance on LLM-generated prompts or expensive manual annotations.\n2. The method provides high-quality experimental support through four comprehensive application studies (self-improvement, capability distillation, reward modeling, content moderation). The experimental results are strong; for example, the self-improvement framework surpasses fine-tuning on real CC3M data, and targeted distillation using fewer than 800 samples improves the TIFA score by 10%.\n3. The paper is well-structured and easy to follow. The five-step process of GENERATE ANY SCENE (from structure enumeration to QA pair conversion) is clearly articulated in Section 2.1. The architectural overview in Figure 1 effectively communicates the proposed framework.\n4. This work is significant as it provides a scalable, low-cost, and highly controllable data generation solution for the text-to-vision field. Its applications (especially low-cost reward modeling and targeted capability distillation) offer efficient and practical pathways for improving and evaluating generative models.", "weaknesses": "1. The paper mentions a \"commonsense plausibility filtering\" in Step 1, but lacks a detailed discussion of how this mechanism is implemented. It is unclear how it balances plausibility with compositional diversity.\n2. The system relies on external metadata sources (like WordNet), which fundamentally limits its output scope and carries the risk of inheriting and amplifying inherent social biases from these sources, which the authors do not discuss.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)