CMAG: Concept-Scaffolded Retrieval for Marketplace Avatar Generation

• URL: http://arxiv.org/abs/2605.18680v1
• Date: Mon, 18 May 2026 17:21:43 GMT
• Title: CMAG: Concept-Scaffolded Retrieval for Marketplace Avatar Generation
• Abstract summary: Metaverse platforms rely on creator-driven marketplaces where avatars are assembled from discrete, taxonomy-labeled 3D assets.<n>We propose textbfCMAG, a concept-scaffolded retrieval and verified composition framework for marketplace generation.
• Score: 0.5131152350448099
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Metaverse platforms rely on creator-driven marketplaces where avatars are assembled from discrete, taxonomy-labeled 3D assets (e.g., tops, bottoms, shoes, accessories) under strict category and topology constraints. While users increasingly expect free-form text control, text-only retrieval is brittle: natural language is ambiguous with respect to platform taxonomies, metadata is often noisy or informal, and independently retrieved components can be stylistically inconsistent or geometrically incompatible. We propose \textbf{CMAG}, a concept-scaffolded retrieval and verified composition framework for marketplace avatar generation. Given a prompt, CMAG first synthesizes an intermediate 3D concept scaffold that disambiguates intent beyond text by providing global spatial and stylistic context. In parallel, a view-aware part discovery module extracts localized visual evidence via prompt decomposition and text-grounded segmentation. A prompt-conditioned taxonomy router enforces category coverage and resolves semantic-to-taxonomic mismatch, after which a hybrid category-wise retriever combines part-based fusion with a concept-residual fallback using feature suppression. Finally, an agentic vision--language model filters and re-ranks candidates across categories and drives an iterative verification loop to assemble prompt-faithful, topologically consistent avatars from catalog assets. We evaluate CMAG on diverse compositional prompts and demonstrate improved retrieval robustness and compositional correctness compared to strong baselines, highlighting the importance of 3D concept scaffolding under prompt ambiguity.

Related papers

• When Generated Images Look Right and Retrieve Wrong: Coverage-Guided Cross-Scale Re-Indexing for Knowledge-Faithful Generative Perception [12.968836079025344]
  We propose CERES, a closed-loop multimodal indexing framework that builds a three-level semantic pyramid.<n>It mines implicit concepts via a co-language-aware router, performs scale-routed cross-attention into a lightweight U-Net generator, and verifies coverage by re-indexing the generated image with the same frozen VLM.<n>On four pansharpening benchmarks across seven settings, CERES delivers the new state of the art with the largest gains where scale variation is most extreme.
  arXiv  Detail & Related papers  (2026-08-21T07:28:01Z)
• ConceptFormer: Learning Adaptive Latent Concepts for Query-Document Alignment in Visual Document Retrieval [62.80735412748694]
  ConceptFormer is a latent concept representation learning framework for visual document retrieval.<n>It query-relevant evidence as continuous, query-conditioned latent concepts that explicitly bridge localized visual evidence and semantic relevance.<n>It achieves 16.7% and 22.1% relative improvements in average NDCG@10 over the strongest visual retrieval baseline and the strongest OCR-based text retrieval baseline, respectively.
  arXiv  Detail & Related papers  (2026-08-16T12:07:12Z)
• Beyond Pixels: Visual Metaphor Transfer via Schema-Driven Agentic Reasoning [56.24016465596292]
  A visual metaphor constitutes a high-order form of human creativity, employing cross-domain semantic fusion to transform abstract concepts into impactful visual rhetoric.<n>We introduce the task of Visual Metaphor Transfer (VMT), which challenges models to autonomously decouple the "creative essence" from a reference image and re-materialize that abstract logic onto a user-specified subject.<n>Our method significantly outperforms SOTA baselines in metaphor consistency, analogy appropriateness, and visual creativity, paving the way for automated high-impact creative applications in advertising and media.
  arXiv  Detail & Related papers  (2026-02-01T17:01:36Z)
• Disentangled Concept Representation for Text-to-image Person Re-identification [4.581495198705364]
  TIReID is challenging due to the substantial modality gap between visual appearances and textual expressions.<n>We propose DiCo, a novel framework that achieves hierarchical and disentangled cross-modal alignment.<n>Our framework achieves competitive performance with state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-01-15T04:08:53Z)
• SaFiRe: Saccade-Fixation Reiteration with Mamba for Referring Image Segmentation [58.80001825332851]
  Referring Image (RIS) aims to segment the target object in an image given a natural language expression.<n>Recent methods predominantly focus on simple expressions like "red car" or "left girl"
  arXiv  Detail & Related papers  (2025-10-11T10:50:58Z)
• MetaFind: Scene-Aware 3D Asset Retrieval for Coherent Metaverse Scene Generation [16.539993197236125]
  We present MetaFind, a scene-aware tri-modal compositional retrieval framework.<n>It is designed to enhance scene generation in the metaverse by retrieving 3D assets from large-scale repositories.
  arXiv  Detail & Related papers  (2025-10-05T06:37:26Z)
• Composed Object Retrieval: Object-level Retrieval via Composed Expressions [71.47650333199628]
  Composed Object Retrieval (COR) is a brand-new task that goes beyond image-level retrieval to achieve object-level precision.<n>We construct COR127K, the first large-scale COR benchmark that contains 127,166 retrieval triplets with various semantic transformations in 408 categories.<n>We also present CORE, a unified end-to-end model that integrates reference region encoding, adaptive visual-textual interaction, and region-level contrastive learning.
  arXiv  Detail & Related papers  (2025-08-06T13:11:40Z)
• ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting [54.92763171355442]
  ObjectGS is an object-aware framework that unifies 3D scene reconstruction with semantic understanding.<n>We show through experiments that ObjectGS outperforms state-of-the-art methods on open-vocabulary and panoptic segmentation tasks.
  arXiv  Detail & Related papers  (2025-07-21T10:06:23Z)
• What You Perceive Is What You Conceive: A Cognition-Inspired Framework for Open Vocabulary Image Segmentation [65.80512502962071]
  Open vocabulary image segmentation tackles the challenge of recognizing dynamically adjustable, predefined novel categories at inference time.<n>Existing paradigms typically perform class-agnostic region segmentation followed by category matching, leading to poor alignment between region segmentation and target concepts.<n>We propose a novel Cognition-Inspired Framework for open vocabulary image segmentation that emulates the human visual recognition process.
  arXiv  Detail & Related papers  (2025-05-26T06:33:48Z)
• Collaborative Group: Composed Image Retrieval via Consensus Learning from Noisy Annotations [67.92679668612858]
  We propose the Consensus Network (Css-Net), inspired by the psychological concept that groups outperform individuals. Css-Net comprises two core components: (1) a consensus module with four diverse compositors, each generating distinct image-text embeddings; and (2) a Kullback-Leibler divergence loss that encourages learning of inter-compositor interactions. On benchmark datasets, particularly FashionIQ, Css-Net demonstrates marked improvements. Notably, it achieves significant recall gains, with a 2.77% increase in R@10 and 6.67% boost in R@50, underscoring its
  arXiv  Detail & Related papers  (2023-06-03T11:50:44Z)
• SrTR: Self-reasoning Transformer with Visual-linguistic Knowledge for Scene Graph Generation [12.977857322594206]
  One-stage scene graph generation approaches infer the effective relation between entity pairs using sparse proposal sets and a few queries. A Self-reasoning Transformer with Visual-linguistic Knowledge (SrTR) is proposed to add flexible self-reasoning ability to the model. Inspired by the large-scale pre-training image-text foundation models, visual-linguistic prior knowledge is introduced.
  arXiv  Detail & Related papers  (2022-12-19T09:47:27Z)
• BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval [61.803481264081036]
  Content-Based Image Retrieval (CIR) aims to search for a target image by concurrently comprehending the composition of an example image and a complementary text. We tackle this task by a novel underlinetextbfBottom-up crunderlinetextbfOss-modal underlinetextbfSemantic compounderlinetextbfSition (textbfBOSS) with Hybrid Counterfactual Training framework.
  arXiv  Detail & Related papers  (2022-07-09T07:14:44Z)
• MOC-GAN: Mixing Objects and Captions to Generate Realistic Images [21.240099965546637]
  We introduce a more rational setting, generating a realistic image from the objects and captions. Under this setting, objects explicitly define the critical roles in the targeted images and captions implicitly describe their rich attributes and connections. A MOC-GAN is proposed to mix the inputs of two modalities to generate realistic images.
  arXiv  Detail & Related papers  (2021-06-06T14:04:07Z)

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.