Generative Adversarial Networks and Image Synthesis

Últimos artículos

• Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

  Xinjie Zhang, Peng Zhang, Shicheng Zheng, Jinghao Guo, Zhaoyang Jia, Yifei Shen, Xun Guo, Yuxuan Luo, Jiahao Li, Wenxuan Xie, Fanyi Pu, Xiaoyi Zhang, Kaichen Zhang, Zongyu Guo, Tianci Bi, Dongnan Gui, Zhening Liu, Zimo Wen, Zihan Zheng, Senqiao Yang, Xiao Li, Jinglu Wang, Bin Li, Yan Lu · 22 de julio de 2026

  Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to-image generation and instruction-based image editing. The stack is built from two co-designed components: Mage-VAE, a l…

• FilmWorld: Agentic Novel-to-Film Generation through Dynamic Cinematic World Modeling

  Jialong Zuo, Haotong Zuo, Shiwei Zhang, Xiang Wang, Chen Li, Nong Sang, Changxin Gao, Xiang Bai · 22 de julio de 2026

  Translating novels into films poses a grand challenge for generative artificial intelligence, requiring conversion of abstract literary prose into long-form, multi-scene visual narratives. While current video generation models excel at short, single-scene clips within narrow temporal and spatial con…

• One Rewrite to Fix Them All? Type-Aware Repair Allocation for Text-to-Image Prompt Optimization

  Haoyue Liu, Xiaoyu Ma, Ye Chen, Shuguang Cui, Xiaoying Tang · 22 de julio de 2026

  Text-to-image (T2I) generators often fail to follow their prompts faithfully, producing wrong counts, swapped attributes, ambiguous relations, and illegible text. Prompt optimization repairs such failures by rewriting the user prompt, requiring no generator retraining, and has yielded promising resu…

• ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling

  Chirag Vashist, Ke Li · 22 de julio de 2026

  Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching. Along the way, the underlying techniques have become more complicated and various beliefs about what drives strong empirical performance have taken hold. Due to the success of diffusion models …

• Signed Rectified Flow: Negativity-Controlled Generation

  Runlong Liao, Baiyu Su, Lizhang Chen, Qiang Liu · 22 de julio de 2026

  We introduce Signed Rectified Flow (Signed RF), a generalization of Rectified Flow that targets the signed measure $\pi^{sign} = (1+\alpha)\pi^+ - \alpha\pi^-$, where $\alpha>0$, $\pi^+$ is the distribution to promote, and $\pi^-$ is the distribution to suppress. Although direct sampling from a sign…

• Local Label-Informed Feature Transfer for Generating Ground-Truth Medical Images: A Comparison of GAN- and Diffusion-Based Approaches

  Rick Wilming, Irem Ozseker, Luca Matteo Cornils, Ahc\`ene Boubekki, Benedict Clark, Danny Panknin, Stefan Haufe · 22 de julio de 2026

  Validating Explainable Artificial Intelligence (XAI) methods in medical imaging requires ground-truth data with known locations of informative features. However, current approaches rely on expert annotations, which are prone to labeling errors, or on hand-crafted artificial perturbations superimpose…

• Appearance Pointers -- Multimodal Region Control of Diffusion Transformers

  Rahul Sajnani, Yulia Gryaditskaya, Radom\'ir M\v{e}ch, Srinath Sridhar, Matheus Gadelha · 22 de julio de 2026

  Controllable image generation remains challenging for creative professionals, who often require precise regional control over materials, object identities, and spatial arrangements that cannot be reliably achieved through text prompting alone. Diffusion Transformers (DiTs) can natively ingest hetero…

• Decafs: Disentangled Conditional adversarial Flows

  Anirudh jain, Sakshi Varshney, Samuel Kaski, Vikas Garg · 22 de julio de 2026

  Flow-based models have established state-of-the-art performance in generative modeling across domains, but are hard to interpret due to their complex latent embeddings. In particular, the entanglement of generative factors in the latent space hinders controlled generation. We circumvent this issue b…

• AlayaWorld: Interactive Long-Horizon World Modeling -- Full Technical Report

  AlayaWorld Team, Kaipeng Zhang, Chuanhao Li, Yifan Zhan, Yongtao Ge, Yuanyang Yin, Jiaming Tan, Kang He, Liaoyuan Fan, Mingliang Zhai, Ruicong Liu, Xiaojie Xu, Xuangeng Chu, Zhen Li, Zhengyuan Lin, Zhixiang Wang, Zian Meng, Zihui Gao · 22 de julio de 2026

  Unlike conventional video game development, which relies on labor-intensive pipelines for asset production, animation, physics, and programming, video world models generate interactive environments from user inputs instantly. It enable us to create customized, explorable, and continuously evolving v…

• Decoder-Preserving Sparse Autoencoders: Which Readouts Survive Sparse Compression?

  Aniket Deshpande · 21 de julio de 2026

  Sparse autoencoders (SAEs) compress model activations into sparse codes, but equal reconstruction error and sparsity can preserve different linearly decodable signals. We formalize this ambiguity as a matrix-valued distortion between optimal ridge-prediction operators and train decoder-preserving SA…

• Learning Spatio-Temporal Foundation Models from Pure Synthetic Data

  Yutong Feng, Shiyuan Piao, Yutong Xia, Xu Liu, Wenqi Fan, Fugee Tsung, See-Kiong Ng, Yuxuan Liang · 21 de julio de 2026

  Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time. However, existing approaches suffer from distributional bias in real-world pre-training data, structural bottlenecks of autoregressive or diffusion-based paradigms…

• Identity-Consistent Expression Fields: A Disentangled Neural Radiance Field Framework for Few-Shot Facial Expression Synthesis

  Minh Tran · 21 de julio de 2026

  Neural Radiance Fields (NeRF) have enabled photorealistic novel-view synthesis of 3D scenes and, in the facial domain, have been extended to reconstruct and animate 3D face models from a small number of images. However, existing few-shot dynamic NeRF methods for facial expression editing typically w…

• Persistent Sparse Autoencoders: Learning Feature Timescales in Language Models

  Haoyan Luo, Mateo Espinosa Zarlenga, Mateja Jamnik · 21 de julio de 2026

  Sparse autoencoders (SAEs) decompose language model activations into sparse features, but standard SAEs encode each token independently and do not expose information that persists across a sequence. We introduce Persistent Sparse Autoencoders (Persistent SAEs), which extend standard SAEs by learning…

• Coordinated Disentanglement with Iterative Mode Discovery Under Hidden Correlations

  Rong Hu, Ling Chen · 21 de julio de 2026

  Disentangled representation learning is a powerful paradigm for robust attribute prediction. While recent methods address attribute correlations, hidden correlations remain underexplored, where data under the value of a certain attribute exhibit underlying modes correlated with other attributes. To …

• LaCache: Exact Caching and Precision-Adaptive Inference for Diffusion Large Language Models

  Xingru Chen, Zelang Liang, Yongjia Ma, Jiqing Zhan, Shuling Yang, Lian Wen, Kun Zhan · 21 de julio de 2026

  Diffusion-based Large Language Models(DLLMs) enable parallel generation via Semi-Autoregressive (SAR) decoding in text generation. However, current methods suffer from severe operator-level redundancy: they recompute the entire sequence during denoising steps, ignoring that the prefix and masked suf…

• Chebyshev Manifold Adaptation

  Jiawen Li · 21 de julio de 2026

  The paper presents a new parameter-efficient adaptation method called ChebyMA (Chebyshev Manifold Adaptation). ChebyMA adopts weight matrices through a multi-surface superposition of Chebyshev polynomial bases evaluated on learnable coordinates and combined via trainable coefficient matrices, replac…

• Three-Body Scattering for Generative Modeling

  Peng Sun, Zhenglin Cheng, Deyuan Liu, Jun Xie, Xinyi Shang, Tao Lin · 21 de julio de 2026

  Modern generative models typically rely on an adversarial critic, a prescribed noise-to-data path, or an autoregressive factorization. Instead, we show that a proper distributional energy can induce sample-level motion and provide direct regression supervision for a one-step generator. Three-Body Sc…

• Program Synthesis for Simulation-Based Inference: Joint Model Selection and Parameter Estimation

  Siddharth Mishra-Sharma · 21 de julio de 2026

  Neural simulation-based inference enables parameter estimation for complex models, but typically requires the user to specify a simulator encoding a fixed model structure. We present a framework for joint model selection and parameter estimation that combines large language models for program synthe…

• A${}^2$BM: Alignment-Aware Bridge Matching for Image-to-Image Translation

  Aimi Okabayashi (UBS Vannes), Georges Le Bellier (LIP, CEDRIC - VERTIGO), Nicolas Audebert (LaSTIG, IGN, CEDRIC - VERTIGO), Charlotte Pelletier (OBELIX), Thomas Corpetti (LETG - Rennes), Nicolas Courty (OBELIX) · 21 de julio de 2026

  Paired image-to-image translation underpins a wide range of computer vision tasks, including image editing, sensor translation, and domain adaptation. Bridge matching and flow matching have recently emerged as powerful frameworks, extending diffusion models to arbitrary source and target distributio…

• SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift

  Jiaqi Zhu, Xincheng Chen, Yuncheng Wu, Zhaojing Luo, Beng Chin Ooi · 21 de julio de 2026

  Generative models trained on a source domain often produce samples that are poorly aligned with shifted target domains, limiting their effectiveness for target-domain data augmentation. Although target-specific adaptation can reduce this mismatch, it typically requires additional optimization and do…

• Semi-Supervised Conditional Diffusion via Label Augmentation

  Jin Su, Yuan Gao, Yong Zhou, Jian Huang · 21 de julio de 2026

  Conditional diffusion models have become a powerful and flexible framework for learning complex conditional distributions from labeled data. In practice, however, acquiring high-quality labels is costly and time-consuming, leaving large volumes of unlabeled data unused. To address this, we introduce…

• Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations

  Changyu Liu, Yuling Jiao, Jian Huang · 21 de julio de 2026

  Conditional generative modeling remains a challenging problem in semi-supervised settings where labeled data is scarce but unlabeled samples are abundant. To effectively leverage structural information embedded within the unlabeled dataset and compensate for sparse conditioning signals, we propose a…

• DiFA: Inference-Time Forward-Process Alignment for Diffusion Models

  Shigui Li, Delu Zeng · 21 de julio de 2026

  The prevailing inference framework for diffusion models formulates generation fundamentally as a problem of numerical integration. This perspective casts the model as an exact estimator, neglecting the inherent statistical uncertainty of the denoising process. In this work, we propose Forward-Proces…

• Semantic Color Naturalness Breaker: Preventing Illegitimate Colorization via Content-Aware Color Priors

  Yuki Nii, Futa Waseda, Ching-Chun Chang, Isao Echizen · 21 de julio de 2026

  Automatic image colorization enables large-scale and low-cost reuse of grayscale media (e.g., manga panels and archival photographs), facilitating unauthorized reuse and redistribution. Once released online, grayscale content can be readily turned into unauthorized colorized derivatives using off-th…

• Loop the Loopies!

  Zitian Gao, Yilong Chen, Yihao Xiao, Xinyu Yang, Ran Tao, Joey Zhou, Bryan Dai · 20 de julio de 2026

  We present Loopie, the most powerful looped Transformer to date. The Loopie series consists of two Mixture-of-Experts (MoE) models: a 20B-parameter model with 2B active parameters and a 6Bparameter model with 0.6B active parameters. Looped Transformers have long faced a challenge: given an N-fold in…