PaperlayerView exact revision on arXiv

Source-linked AI summary

Generated Contents Enrichment

Mahdi Naseri, Jiayan Qiu, Zhou Wang

arXiv:2405.03650v4cs.CVcs.LG

TL;DR

Sparse descriptions omit much of the objects and relations in real scenes, so this paper explicitly enriches scene graphs before image generation. Its jointly trained framework produces enriched images that preserve key input elements while adding semantically richer content.

  • Problem

    Sparse scene descriptions omit rich objects and relations, while conventional generators do not expose added content as an inspectable intermediate representation.

  • Method

    A jointly trained adversarial framework enriches scene graphs by predicting additional objects and inter-object relations before downstream image generation.

  • Results

    Enriched images preserve key input elements while adding semantically richer content aligned with the input description.

  • Takeaways & Limitations

    Explicit graph-level enrichment provides an inspectable intermediate representation evaluated with both graph-side and image-side evidence.

  • Takeaways & Limitations

    The framework inherits adversarial-generation limitations, including less stable training and potentially expensive training and sampling.

Abstract

from arXiv · show

We study Generated Contents Enrichment (GCE), a conditional image-generation task in which a sparse scene description is first enriched through an explicit scene representation and then rendered into semantically richer visual content. Conventional image-generation systems can produce visually realistic outputs from limited scene descriptions, but the added content is usually implicit in the generator rather than represented as an inspectable intermediate structure. In contrast, GCE seeks to make scene enrichment explicit at the scene-representation level while examining its visual consequences during generation, with the goal of encouraging generated content that is visually plausible, structurally coherent, and semantically richer than the sparse input. To instantiate GCE, we propose a jointly trained adversarial framework that enriches scene graphs by modeling object semantics and inter-object relations. Our approach first represents the input description as a scene graph, where nodes model objects and edges capture inter-object relations. The framework uses graph convolutional networks to predict additional objects and their relations to the existing scene. Finally, the enriched scene graph is passed through the downstream image-generation pipeline to generate the corresponding visual content. We evaluate the framework with proxy scene graph enrichment metrics, image-quality comparisons, qualitative examples, and user studies on the Visual Genome dataset.

1 Introduction

Generated Contents Enrichment (GCE) explicitly enriches a scene graph from a sparse description with plausible objects and relations before rendering visual content. The proposed jointly trained framework uses graph-based enrichment and visual-domain training signals to produce semantically and structurally coherent images, evaluated on Visual Genome.

  • Task definition and distinction: GCE addresses the semantic richness gap by making added objects and inter-object relations explicit in an inspectable scene graph before image synthesis.The enriched graph must be plausible, compatible with the seed description, and useful for downstream generation.
  • Task definition and distinction: Unlike methods that complete, generate, or render scene graphs—or expand text—GCE follows the full path from sparse description and seed graph to enriched graph and rendered content.Its defining distinction is explicit graph-level enrichment coupled with evaluation of both the enriched graph and the rendered image.
  • Framework: The framework iteratively adds a temporary object slot and predicts its object and incident relation labels with a Graph Convolutional Network.The GCN aggregates information from known nodes and edges, while scene graph critics encourage realistic object-relation structure.
  • Framework: The enriched graph is converted into continuous text conditioning and rendered by a fixed text-to-image generator, while visual scene and image-text signals train the enrichment modules.These training signals encourage generated images to remain plausible and consistent with the input description.
  • Contributions and evaluation: The enriched image preserves key input elements while adding semantically and structurally coherent content, and evaluation uses Visual Genome proxy metrics, image-quality comparisons, qualitative examples, and user studies.The framework predicts new objects and relations and evaluates their visual effects during training.

2 Related Work

Prior work spans image-generation models and scene-graph-based understanding and synthesis, but typically either conditions on a specified graph or enriches graphs from observed images. GCE instead makes scene-graph enrichment an explicit intermediate step before image synthesis and evaluates its effects on both graph structure and rendered images.

  • Image Generation: GANs, VAEs, autoregressive models, diffusion models, and hybrid latent approaches offer alternative image-generation paradigms with distinct quality, stability, or computational tradeoffs.GAN training can be unstable and may not model the full data distribution; VAEs often have lower perceptual quality, while autoregressive and diffusion methods can be computationally expensive.
  • Scene Graphs: Scene graphs represent objects, relations, and attributes, supporting higher-level scene understanding and applications including recognition, captioning, retrieval, and image synthesis.Existing scene-graph-to-image methods generally condition on a provided graph.
  • Generated Contents Enrichment: GCE inserts explicit enrichment before synthesis, adding objects and relations under scene-semantic and image-consistency constraints rather than conditioning on an already complete graph.This distinguishes GCE from graph-to-image methods that assume the conditioning graph is specified.
  • Generated Contents Enrichment: Unlike image-conditioned enrichment and outpainting, GCE enriches a sparse graph before image synthesis rather than inferring added structure from an observed image.The neighboring image-based methods start from an input image and produce or extend a richer scene graph.
  • Generated Contents Enrichment: GCE evaluates enriched objects and relations both as graph outputs and through their downstream effects on rendered images, unlike graph-focused expansion methods.The framework therefore evaluates the full path from sparse description to enriched representation to rendered image.

3 Method

The method makes scene enrichment explicit by transforming a sparse scene description into an enriched scene graph before image synthesis. It uses graph-based prediction, adversarial graph criticism, and training-time visual alignment to produce richer, coherent images.

  • Task formulation: GCE maps a sparse scene description to a reduced scene graph, enriches that graph, and renders the enriched representation into an image.The framework is formalized as g = T(x), ĝ = G_sg(g), and Î = G_im(ĝ).
  • Pipeline: The three-stage pipeline enriches scene graphs, generates images from enriched graphs, and supplies training-time visual alignment signals.At inference, only the learned enricher and image renderer are used; the critic and Stage 3 modules are training-only.
  • Scene graph enrichment: The scene graph enricher uses GCNs and MLPs to predict an added object and its relations to the existing graph.For an iterative step, it predicts an added object ô_t and incident relations Ê_t connected to current graph objects.
  • Scene graph enrichment: A scene graph critic with local and global discriminators encourages enriched content to remain realistic, structurally coherent, and semantically meaningful.The critic evaluates enriched parts both independently and within the full scene graph.
  • Image generation and alignment: The downstream generator synthesizes an image with richer relevant content from the enriched scene graph, while alignment modules preserve plausibility and consistency with the original scene.The alignment signals come from a scene classifier and image/text encoders during training.

Appendix Section A.1 summarizes the layer-level GConv update used here.

The Scene Graph Enricher progressively augments an input graph by predicting one object, an incident edge, and its predicate. Its object-prediction component uses a deeper GCN with additional layers and skip connections.

  • Layer-level GConv update: The enricher predicts an added object, scores directed incident edges, and predicts the selected edge’s predicate in three successive steps.These modules are G_sg,obj, G_sg,edge, and G_sg,pred, respectively.
  • Layer-level GConv update: The three predictions introduce new objects, edges, and predicates, progressively enriching the scene graph.The resulting graph can support further enrichment iterations.
  • Layer-level GConv update: G_sg,obj uses the proposed GCN with additional layers and skip connections to deepen the network while avoiding vanishing-gradient issues.The predicted object is added to the original graph before subsequent enrichment steps.

4 Experiments

Experiments evaluate GCE through graph-enrichment proxies, image-quality comparisons, ablations, and user studies on Visual Genome, using a fixed Stable Diffusion renderer for image comparisons. The reported results show stronger graph recovery, improved image metrics, and user evidence that enrichment adds meaningful content while preserving coherence and realism.

  • Dataset and setup: All image comparisons use the fixed Stable Diffusion synthesizer, attributing differences to the sparse input or enrichment method rather than different generators.Both simple and enriched scene representations are converted to text before rendering.
  • Dataset and setup: Visual Genome provides 110k images, 178 object categories, 45 predicate categories, and approximately 62.5k training, 5.5k validation, and 5k test samples.The dataset contains 3.8M objects and 2.3M relations across diverse environments and configurations.
  • Graph enrichment results: Across 10 object-removal seeds, the method achieves the strongest primary recovery scores, including object, edge, predicate, triplet precision, recall, and F1 measures.Compared with SceneGraphGen+, available-edge accuracy rises from 36.64 to 75.18, available-predicate accuracy from 14.17 to 42.20, and triplet precision from 4.87 to 31.69.
  • Image quality and ChatGPT comparisons: The graph-based enriched variant improves the reported IS and FID values over Simple and outperforms three ChatGPT prompt variants on the reported comparisons.The ChatGPT variants remain below Simple, and their reported FID values worsen as prompts expand from Direct to Scene to Object.
  • Ablations: Ablation differences are modest in proxy metrics, indicating that modules contribute jointly within the selected framework rather than being fully isolated by these measures.Because the task lacks a unique solution, the proxy results should be interpreted comparatively and cautiously.
  • User studies: Participants selected synthesized images as more realistic in only 20.09% of comparisons, while enriched images were often judged description-consistent and more frequently preferred against corresponding real images.These findings support added content that remains compatible with the overall scene and preserves scene coherence.

5 Conclusion

The paper proposes an adversarial GCN-based framework for Generated Contents Enrichment that explicitly enriches scene representations before image generation. Its results support explicit scene enrichment as feasible while identifying broader graph-annotated datasets and stronger renderers as natural extensions.

  • Conclusion: The adversarial GCN-based framework enriches the scene representation underlying the input description using a multi-term objective.It uses visual-domain signals during training to guide generation of richer output images.
  • Conclusion: The framework narrows the gap between sparse and richer scene descriptions while keeping added visual content compatible with the original scene.It is designed to preserve the key scene elements specified in the input description.
  • Conclusion: A pair of discriminators and alignment modules encourage appended objects and relations to remain structurally and semantically coherent with the original scene.These components support compatibility between enriched content and the input scene.
  • Conclusion: Overall, the results support GCE as a feasible direction for explicit scene enrichment before image generation, with broader graph-annotated datasets and stronger renderers as natural extensions.The conclusion identifies these extensions as future directions.

Online Appendix · A Supplementary Details · A.1 GConv Operation

The GConv operation updates relation-triplet representations, aggregates candidate node updates across incident triplets, and combines the result with a skip connection for subsequent processing.

  • A.1 GConv Operation: GConv layers first update relation-triplet representations before performing node-level aggregation.
  • A.1 GConv Operation: For each triplet (o_i,r,o_j), the subject, predicate, and object vectors are concatenated and mapped through fully connected layers.
  • A.1 GConv Operation: The mapped triplet representation is divided into candidate subject, edge, and object updates.
  • A.1 GConv Operation: Candidate node updates are averaged over all incident triplets.
  • A.1 GConv Operation: The aggregated candidate node updates pass through additional fully connected layers.
  • A.1 GConv Operation: A skip connection combines the transformed updates before the next processing stage.

A.2 GCN Implementation

The GCN implementation processes relation triplets from multiple scene graphs in parallel while preserving graph separation within each mini-batch. Its Enriching Edge Detector predicts directed candidate edges from subject- and object-side node embeddings.

  • Mini-batch processing: Relation triplets from all graphs are processed in parallel within mini-batches, using a dummy image node and image-link predicates to keep scene graphs separated.The image-link predicates preserve graph boundaries during joint mini-batch processing.
  • Edge detection: The Enriching Edge Detector maps each object node and its hidden vector to separate subject-side and object-side embeddings using two MLPs.These embeddings provide distinct representations for the two endpoints of a directed relation.
  • Edge detection: Directed candidate edges are scored by inner products between the subject-side and object-side embeddings.The detector therefore evaluates candidate relations through pairwise embedding compatibility.

A.3 Generator GCN Architectures

The generator uses two separately weighted GCNs with the same architecture, expanding and then compressing graph-context embeddings before returning to the output dimension.

  • The generator uses two GCNs with identical architectures but separate weights.
  • The architecture expands embedding dimensions to process graph context, reduces them for compact encoding, and returns to the output dimension.
  • The final configuration includes object and predicate embedding layers, two GCN classifier layers, and two enriching-edge layers.

A.4 CLIP Text Encoder … A.10 Reproducibility Package

The appendix specifies the text, rendering, evaluation, discriminator, training, and reproducibility procedures for GCE. It details how scene graphs are converted and enriched, how models are tuned and evaluated, and what artifacts are released.

  • A.4 CLIP Text Encoder: CLIP ViT-B/32 encodes graph-derived text formed from relation phrases and standalone object labels, with duplicate removal, shuffling, and a 75-word budget.The same graph-to-text conversion supports CLIP alignment and Stable Diffusion prompts.
  • A.6 Image Generator: Image generation uses Diffusers StableDiffusionPipeline with checkpoint stabilityai/stable-diffusion-2-1, fp16 CUDA inference, and guidance-scale candidates 7.5, 9, 15, and 25.Renderer and encoder parameters remain fixed during GCE training, so enrichment components receive the updates.
  • A.7 Extended Proxy Scene Graph Enrichment Metrics: Proxy enrichment metrics evaluate object and edge recovery, predicate correctness, no-edge accuracy, triplet precision/recall/F1, and MEP+.Triplet relation credit is gated by correct recovery of the added object, while MEP+ measures support from comparable Visual Genome training contexts.
  • A.8 Pair of Discriminators: Discriminator tuning varies embedded dimensions from 16 to 256 and update frequency, selecting a smaller discriminator than generator and fewer discriminator updates.The procedure addresses the finding that distinguishing enriched graphs from original data is usually easier than enrichment itself.
  • A.10 Reproducibility Package: The reproducibility package provides preprocessing, reduced-graph evaluation, hyperparameters, metric scripts, prompts, image artifacts, seed aggregation, and reproduction commands.It also preserves tuning records and additional qualitative examples.
Loading 2405.03650v4…