InnoAds-Composer: Efficient Condition Composition for E-Commerce Poster Generation

• URL: http://arxiv.org/abs/2603.05898v1
• Date: Fri, 06 Mar 2026 04:36:33 GMT
• Title: InnoAds-Composer: Efficient Condition Composition for E-Commerce Poster Generation
• Abstract summary: We propose a single-stage framework that enables efficient tri-conditional control tokens over subject, glyph, and style.<n>InnoAds-Composer significantly outperforms existing product poster methods without obviously increasing inference latency.
• Score: 27.024838488122104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: E-commerce product poster generation aims to automatically synthesize a single image that effectively conveys product information by presenting a subject, text, and a designed style. Recent diffusion models with fine-grained and efficient controllability have advanced product poster synthesis, yet they typically rely on multi-stage pipelines, and simultaneous control over subject, text, and style remains underexplored. Such naive multi-stage pipelines also show three issues: poor subject fidelity, inaccurate text, and inconsistent style. To address these issues, we propose InnoAds-Composer, a single-stage framework that enables efficient tri-conditional control tokens over subject, glyph, and style. To alleviate the quadratic overhead introduced by naive tri-conditional token concatenation, we perform importance analysis over layers and timesteps and route each condition only to the most responsive positions, thereby shortening the active token sequence. Besides, to improve the accuracy of Chinese text rendering, we design a Text Feature Enhancement Module (TFEM) that integrates features from both glyph images and glyph crops. To support training and evaluation, we also construct a high-quality e-commerce product poster dataset and benchmark, which is the first dataset that jointly contains subject, text, and style conditions. Extensive experiments demonstrate that InnoAds-Composer significantly outperforms existing product poster methods without obviously increasing inference latency.

Related papers

• simpleposter: a simple baseline for product poster generation [17.46071085989295]
  We present SimplePoster, a simple yet effective inpainting-based framework that achieves faithful subject preservation and accurate, position-controllable text rendering without external controllers.<n> Experiments show that SimplePoster achieves a $98.7%$ subject preservation rate, compared to $55.2%$ for SeedEdit 3.0 and $85.3%$ for PosterMaker, while also improving text rendering accuracy.
  arXiv  Detail & Related papers  (2026-05-09T08:12:55Z)
• Text-Image Conditioned 3D Generation [71.98375600100856]
  TIGON is a minimalist dual-branch baseline with separate image- and text-conditioned backbones and lightweight cross-modal fusion.<n>Our diagnostic study shows that even simple late fusion of text- and image-conditioned predictions outperforms single-modality models.<n>Extensive experiments show that text-image conditioning consistently improves over single-modality methods.
  arXiv  Detail & Related papers  (2026-03-22T15:36:16Z)
• EfficientPosterGen: Semantic-aware Efficient Poster Generation via Token Compression and Accurate Violation Detection [31.15442597866692]
  EfficientPosterGen is an end-to-end framework for automated academic poster generation.<n>It introduces semantic-aware retrieval and token-efficient multimodal generation.<n>It achieves substantial improvements in token efficiency and layout reliability.
  arXiv  Detail & Related papers  (2026-02-25T17:03:39Z)
• Unified Text-Image Generation with Weakness-Targeted Post-Training [57.956648078400775]
  Unified multimodal generation architectures that jointly produce text and images have recently emerged as a promising direction for text-to-image (T2I) synthesis.<n>This work explores post-training to achieve fully unified text-image generation, where models autonomously transition from textual reasoning to visual synthesis.
  arXiv  Detail & Related papers  (2026-01-07T19:19:44Z)
• Geometric Disentanglement of Text Embeddings for Subject-Consistent Text-to-Image Generation using A Single Prompt [14.734857939203811]
  We propose a training-free approach that addresses semantic entanglement from a subject perspective.<n>Our approach significantly improves both subject consistency and text alignment over existing baselines.
  arXiv  Detail & Related papers  (2025-12-18T11:55:06Z)
• JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation [10.00677022779314]
  We propose a unified multimodal framework that encodes images and captions into discrete visual and textual tokens.<n>We incorporate cross-attention after the feature predictor for conditional denoising while maintaining a task-agnostic backbone.<n>Our approach shows that late architectural fusion combined with objective-level alignment offers an effective balance between conditioning strength and backbone generality in token-based T2I.
  arXiv  Detail & Related papers  (2025-10-01T14:51:10Z)
• UniGlyph: Unified Segmentation-Conditioned Diffusion for Precise Visual Text Synthesis [38.658170067715965]
  We propose a segmentation-guided framework that uses pixel-level visual text masks as unified conditional inputs.<n>Our approach achieves state-of-the-art performance on the AnyText benchmark.<n>We also introduce two new benchmarks: GlyphMM-benchmark for testing layout and glyph consistency in complex, and MiniText-benchmark for assessing generation quality in small-scale text regions.
  arXiv  Detail & Related papers  (2025-07-01T17:42:19Z)
• PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering [50.76106125697899]
  Product posters, which integrate subject, scene, and text, are crucial promotional tools for attracting customers.<n>Main challenge lies in accurately rendering text, especially for complex writing systems like Chinese, which contains over 10,000 individual characters.<n>We develop TextRenderNet, which achieves a high text rendering accuracy of over 90%.<n>Based on TextRenderNet and SceneGenNet, we present PosterMaker, an end-to-end generation framework.
  arXiv  Detail & Related papers  (2025-04-09T07:13:08Z)
• PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides [51.88536367177796]
  We propose a two-stage, edit-based approach inspired by human drafts for automatically generating presentations.<n>PWTAgent first analyzes references to extract slide-level functional types and content schemas, then generates editing actions based on selected reference slides.<n>PWTAgent significantly outperforms existing automatic presentation generation methods across all three dimensions.
  arXiv  Detail & Related papers  (2025-01-07T16:53:01Z)
• GlyphDraw2: Automatic Generation of Complex Glyph Posters with Diffusion Models and Large Language Models [7.152732507491591]
  We propose an automatic poster generation framework with text rendering capabilities leveraging LLMs.<n>This framework aims to create precise poster text within a detailed contextual background.<n>We introduce a high-resolution font dataset and a poster dataset with resolutions exceeding 1024 pixels.
  arXiv  Detail & Related papers  (2024-07-02T13:17:49Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Models [8.250234707160793]
  Recent text-to-image (T2I) diffusion models show outstanding performance in generating high-quality images conditioned on textual prompts. They fail to semantically align the generated images with the prompts due to their limited compositional capabilities. We propose a novel attention mask control strategy based on predicted object boxes to address these issues.
  arXiv  Detail & Related papers  (2023-05-23T10:49:22Z)
• eDiffi: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers [87.52504764677226]
  Large-scale diffusion-based generative models have led to breakthroughs in text-conditioned high-resolution image synthesis. We train an ensemble of text-to-image diffusion models specialized for different stages synthesis. Our ensemble of diffusion models, called eDiffi, results in improved text alignment while maintaining the same inference cost.
  arXiv  Detail & Related papers  (2022-11-02T17:43:04Z)
• Long Document Summarization with Top-down and Bottom-up Inference [113.29319668246407]
  We propose a principled inference framework to improve summarization models on two aspects. Our framework assumes a hierarchical latent structure of a document where the top-level captures the long range dependency. We demonstrate the effectiveness of the proposed framework on a diverse set of summarization datasets.
  arXiv  Detail & Related papers  (2022-03-15T01:24:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.