Papers Image Generation
“Image Generation” 태그가 달린 논문 7,966편 · 필터 해제
UltraText Bench: A Comprehensive Bilingual Benchmark for Evaluating Visual Text Rendering in Image Generation
Dense visual text requires image generators to reproduce long strings across multiple regions with correct placement and legibility. As short-string rendering improves, evaluation must test sustained performance across m…
Image GenerationInverting Multi-Vector Visual Document Indices
Prevailing multi-vector visual document retrievers store each page as about a thousand patch vectors, often in vector databases run by a third party. Since no one can read a page from its vectors, this index is easily tr…
Image GenerationSphere Encoder 2
Sphere Encoder is an autoencoder that generates images by decoding random points from a high-dimensional latent sphere. We identify two limitations of the original formulation that reduce its generation quality. First, r…
Image GenerationEmbedding Prediction Helps Image Generation
In diffusion transformers, a class label or a text prompt is embedded once, and the same condition is reused at every denoising step. We ask whether predicted embeddings can serve as this condition instead. Next-Embeddin…
Image GenerationForm and Void: Entangled Composition through an Autonomous AI Agent
Positive and negative space is a fundamental principle in visual composition, supporting visually coherent forms and layered semantic relationships. Generating such compositions is challenging because it requires coordin…
Image GenerationVIEScore2: Unified Image Evaluation with Spatially Grounded Explanations
Existing synthetic image evaluators typically provide only a scalar quality score and do not identify the image regions that support it. We introduce VIEScore2, a unified evaluator for image generation and editing tasks …
Image GenerationUniEvo-VL: An On-policy Self-Distillation Training Recipe for Multimodal Model Self-improvement
Modern multimodal models bring generation and understanding into a single unified system, which enables them to provide and learn from their own feedback. Motivated by this unified capacity, we introduce UniEvo-VL, a sel…
Image GenerationMultimodal Flow: Unified Flow Modeling of Language and Vision in Embedding Spaces
We present Multimodal Flow, a fully continuous generative model of language and vision. Most unified multimodal models either model both language and quantized images as discrete tokens or combine discrete language predi…
Image GenerationThink Before You Score: Thinking Reward Model for Visual Generation
Visual reward models are essential for evaluating and improving visual generation models, yet existing approaches typically map task conditions and candidate outputs directly to scalar rewards, leaving implicit what shou…
Reinforcement LearningImage GenerationLearning Native Reflection in Unified Models with Interleaved Reinforcement Learning
Unified multimodal models can both look at and render images, so in principle they can repair their own generations: diagnose what an image gets wrong, revise it, observe the result, and diagnose again. Whether a revisio…
Reinforcement LearningImage GenerationAutoRef: Harness Optimization for Agentic Multi-Reference Image Generation
Recent image generation models can take multiple reference images as input and combine them into a new image. However, multi-reference image generation remains challenging: models may omit or duplicate subjects from the …
Image GenerationPersistence Forcing: Exploiting Feature Specialization in Pixel-Space Diffusion
Pixel-space diffusion Transformers (DiTs) directly operate on high-dimensional visual data, yet their hidden representations typically undergo uniform refinement across depth. Natural images, however, are inherently orga…
Image GenerationDroneWAM: Efficient World Action Model for Drone Visual Navigation
World-action models give visual navigation agents a way to anticipate how candidate actions will change future observations and to act from the predicted consequences. For drones, this capability must operate under tight…
Visual NavigationImage GenerationFuseReg: Regularizing Layer Fusion Mitigates the Reconstruction-Generation Gap in Representation Autoencoders
Representation autoencoders (RAEs) reuse features from a pretrained visual encoder as reconstruction and diffusion latents, integrating strong visual representations into image generation. However, RAEs still need to dec…
Image GenerationViRDM: Taming Representation Distribution Matching for Few-Step Causal Video Generation
Few-step autoregressive (AR) video diffusion enables low-latency streaming generation, but existing post-training methods predominantly rely on Distribution Matching Distillation (DMD), requiring both a large pretrained …
Image GenerationVideo GenerationStableVQ: Practical Guidelines for Stable Vector-Quantized Tokenizer Training
Vector Quantization (VQ) is fundamental to discrete visual tokenizers that power modern autoregressive and masked image generation models. While recent shared-projection codebook methods have substantially advanced codeb…
Image GenerationUFO: Chain-of-Evaluation for Omni-Condition Alignment in Multi-Modal Image Generation
Multi-modal image generation, particularly subject-driven customization, has garnered growing attention in recent years. Despite the rapid advancement of generative models, their evaluation remains largely lagging. Exist…
Image GenerationPaint-Anything: Unified Any-Color Control for Image Generation and Editing
Professional design requires any-color control: the ability to specify an object's target color with any 24-bit hex value for image generation and editing. Prior work has explored color generation, editing, and colorizat…
Image GenerationFRPSS: Feature Rearrangement in Pre-Shape Space for Single-Image Generation
Generative models trained on a single image often struggle to balance global structural integrity and local diversity. Existing single-image generation methods commonly rely on random noise to drive the generation proces…
Image GenerationReasoning with Image Generation
Chain-of-thought reasoning has revolutionized natural language processing by enabling large language models (LLMs) to decompose problems into intermediate steps before answering. Yet confining reasoning to the textual do…
Spatial ReasoningImage GenerationDepth EstimationVisual Reasoning