Grounding Free-Form Instructions for Fashion Complementary Image Generation

FlowLess: Controlling Abstract Image Generation

GenPuzzle: Benchmarking Visual Reasoning in Image Generation Models

Recent image generation systems increasingly combine multimodal understanding, reasoning, and synthesis, suggesting that they may do more than render plausible scenes. Yet existing evaluations emphasize aesthetics, prompt alignment, compositionality, or text-based answers, leaving unclear whether these systems can solve visual problems and faithfully express solutions in pixels. We introduce GenPuzzle, a benchmark for reasoning-centric image generation. GenPuzzle contains 2,005 problems across 12 tracks, spanning pattern completion, spatial construction, mazes, Sudoku, nonograms, tangrams, board games, matchstick puzzles, orthographic projection, and mathematical visual proof. Each task provides a visual puzzle and requires an image output that preserves the input state while executing a logically valid solution. GenPuzzle uses task-specific evaluation protocols: discrete grid outputs are transcribed and verified programmatically, while visually complex outputs are assessed with tiered, multidimensional, or binary multimodal large language model (MLLM) rubrics. We further select the automatic judge by measuring agreement with human reference scores. Across three frontier generators, the strongest model reaches only 40.57 Macro Overall, revealing frequent failures in logic, geometry, state preservation, and instruction execution. GenPuzzle provides a testbed for measuring progress from image rendering toward visual problem solving.

RA-CoA: Training-free Fashion Image Captioning via Retrieval-Augmented Chain-of-Attributes

Fashion Image Captioning (FIC) plays a vital role in enhancing user experience and product search in e-commerce platforms. Unlike natural scene image captioning, FIC requires fine-grained visual reasoning and knowledge of domain-specific terminology to capture subtle attributes such as neckline and closure types, graphic patterns, and dress silhouettes. Moreover, as fashion inventories evolve rapidly with new trends, styles, and frequently emerging vocabulary, developing training-free captioning solution becomes essential for scalability and real-world adaptability. Instruction-tuned vision-language models (VLMs) offer a promising solution to fashion image captioning dueto their strong zero-shot capabilities and natural language fluency. However, these general-purpose models often lack attribute-level coverage and precision, and tend to hallucinate or misidentify fine-grained fashion details, making them less suitable for high-fidelity applications like product cataloging or personalized recommendations. To address this, we propose RA-CoA (Retrieval-Augmented Chain-of-Attributes), a novel, training-free framework that disentangles fashion image captioning into two interpretable stages: (i) retrieval of relevant attribute sets from a product knowledge base, and (ii) attribute-level reasoning to generate the final caption. RA-CoA is a model-agnostic approach that works with frozen VLMs to improve fine-grained attribute precision in product captions without the need for fine-tuning. Extensive evaluations across diverse VLM model families under different prompting paradigms demonstrate that RA-CoA significantly improves caption quality, achieving an average gain of 26.3% METEOR score over zero-shot captioning. We make our code publicly available.

TYPO: Instruction-Dense Visual Jailbreaks against Commercial Closed-Source Image-Generation Models

This paper introduces the concept of instruction-dense visual jailbreaks, in which image-generation models produce detailed, readable, and actionable harmful instructions within images, and proposes TYPO, a black-box framework that exploits this safety gap by automatically generating adversarial TYPOgraphy prompts.

Pose-Star++: Semantic-Visual Understanding for Fine-Grained Fashion Image Editing.

Fashion image editing demands high-dimensional, fine-grained control to follow personalized, unpredictable natural-language instructions. Yet current methods are limited by a fundamental trade-off: fashion-specific approaches offer structural accuracy but lack semantic flexibility, while general text-driven editors are semantically flexible but structurally inaccurate. To bridge this gap, we propose Pose-Star++, a training-free, plug-and-play framework that introduces two core innovations: an LVLM-based Understanding Module that shifts from word- to sentence-level semantic-visual comprehension, eliminating cumbersome instruction pre-parsing and enabling robust understanding of complex natural language; a Bidirectional Calibration Module that co-optimizes semantic and structural constraints through forward pose-guided and backward attention-guided refinement, achieving precise, whole-body-reachable region calibration even under challenging in-the-wild poses. We further contribute the first real-world-oriented fashion-editing benchmark with diverse data, instructions, and tasks, exposing long-overlooked practical challenges. Extensive experiments demonstrate that Pose-Star++ significantly outperforms existing methods in semantic alignment, pose robustness, and in-the-wild generalization across complex scenarios, advancing toward practical, user-guided fashion creation.

Structured context augmentation for multimodal LLMs in character art analysis