Recent progress in video-to-video (V2V) translation has enabled realistic resimulation of embodied AI demonstrations, a capability that allows pretrained robot policies to be transferable to new environments without additional data collection. However, prior works can only operate on a single view at a time, while embodied AI tasks are commonly captured from multiple synchronized cameras to support policy learning. Naively applying single-view models independently to each camera leads to inconsistent appearance across views, and standard transformer architectures do not scale to multi-view settings due to the quadratic cost of cross-view attention. We present VideoWeaver, the first multimodal multi-view V2V translation framework. VideoWeaver is initially trained as a single-view flow-based V2V model. To achieve an extension to the multi-view regime, we propose to ground all views in a shared 4D latent space derived from a feed-forward spatial foundation model, namely, Pi3. This encourages view-consistent appearance even under wide baselines and dynamic camera motion. To scale beyond a fixed number of cameras, we train views at distinct diffusion timesteps, enabling the model to learn both joint and conditional view distributions. This in turn allows autoregressive synthesis of new viewpoints conditioned on existing ones. Experiments show superior or similar performance to the state-of-the-art on the single-view translation benchmarks and, for the first time, physically and stylistically consistent multi-view translations, including challenging egocentric and heterogeneous-camera setups central to world randomization for robot learning.
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Narrative image generation aims to create images featuring multiple distinct characters while capturing their interrelationships, posing significant challenges for current text-to-image diffusion models. As a result, general personalized methods often suffer from poor semantic alignment, identity blending, and aesthetic implausibility.These issues are inadequately captured by existing evaluation metrics such as CLIP, ArcFace, and conventional reward models, which fundamentally fail to align with human perceptual preferences. To align with human preferences, we first construct a fine-grained human preference dataset, NI-RLHF, by collecting both detailed human critiques and preference judgments across three core dimensions: prompt following, identity consistency, and visual quality.This comprehensive dataset facilitates the training of NIReward, a critique-based reward model capable of generating interpretable image evaluations.Building upon the interpretable reward signal from NIReward, we propose Adaptive Dominance-based Preference Optimization (ADPO) to balance learning across diverse preference dimensions while dynamically adapting to reward margins.Experimental results indicate that NIReward significantly outperforms existing evaluation models and reward models, and ADPO yields a significant improvement across the three key preference dimensions. By introducing NIReward and ADPO, our work paves the way for generating narrative images aligned with actual human preferences.
Explicit 3D representations have already become an essential medium for 3D simulation and understanding. However, the most commonly used point cloud and 3D Gaussian Splatting (3DGS) each suffer from non-photorealistic rendering and significant degradation under sparse inputs. In this paper, we introduce Sparse to Dense lifting (S2D), a novel pipeline that bridges the two representations and achieves high-quality 3DGS reconstruction with minimal inputs. Specifically, the S2D lifting is two-fold. We first present an efficient one-step diffusion model that lifts sparse point cloud for high-fidelity image artifact fixing. Meanwhile, to reconstruct 3D consistent scenes, we also design a corresponding reconstruction strategy with random sample drop and weighted gradient for robust model fitting from sparse input views to dense novel views. Extensive experiments show that S2D achieves the best consistency in generating novel view guidance and first-tier sparse view reconstruction quality under different input sparsity. By reconstructing stable scenes with the least possible captures among existing methods, S2D enables minimal input requirements for 3DGS applications.
Generative data augmentation has created new opportunities for improving image recognition models. Despite these advances, most existing augmentation methods process images holistically, without accounting for the uneven distribution of discriminative information in classification tasks, where foreground subjects typically contain richer category-relevant signals than background context. Ignoring this imbalance can introduce unintended semantic shifts in generated samples, thereby weakening the model's ability to capture the intrinsic ontology of the target object. In fact, human perception operates in a similar manner: subject identity remains stable, while contextual variations are naturally tolerated as long as overall coherence is preserved. Inspired by this observation, we propose OntoAug, an ontology-oriented data augmentation framework that explicitly distinguishes between subject and environment. OntoAug separates foreground and background through structured layout control and guides diffusion models to generate samples with consistent subjects and diverse contextual variations. By explicitly introducing foreground masks and diverse background prompts during the generation process, the framework enhances both semantic fidelity and diversity in the synthesized data. Extensive experiments show that OntoAug achieves strong performance across multiple tasks, including image classification, few-shot learning, weakly supervised object localization (WSOL), and large vision-language model reasoning.
UniEdit-I: Training-free Image Editing for Unified VLM via Iterative Understanding, Editing and Verifying
PDF ↗Recent advances in diffusion models and vision-language models (VLMs) have significantly enhanced the controllability of image editing. Methods like FlowEdit enable step-by-step editing along a visible, noise-free trajectory, where each intermediate result is a clear image, eliminating the need for full noise inversion. However, these approaches still operate in pixel space or VAE latent space, where intermediate outputs often suffer from visual artifacts, distortions, or unrealistic details--making reliable semantic evaluation difficult. Furthermore, they remain open-loop systems, applying static edits without feedback to guide or correct the editing process adaptively. We propose UniEdit-I, the first training-free, closed-loop image editing framework that operates entirely within the semantic latent space of a unified VLM by introducing an Understanding-Editing-Verifying (UEV) loop:(1) Understanding: parses the source image and editing instruction into a structured source prompt and a minimal target specification;(2) Editing: applies dynamic semantic offsets, with a configurable feedback weighting mechanism that adaptively modulates editing intensity based on real-time alignment feedback;(3) Verifying: leverages the VLM's own multimodal reasoning capability to evaluate the intermediate output along multiple semantic dimensions and trigger early stopping or refinement. By transforming the VLM from a post-hoc evaluator into an in-process conductor, UniEdit-I establishes the first semantics-driven, self-correcting closed-loop image editing pipeline. Evaluated on GEdit-Bench, UniEdit-I achieves state-of-the-art performance without any fine-tuning or architectural modifications, and even surpasses several large-scale pre-trained editors.
BoostSLT: Boosting Sign Language Translation via a Plug-and-Play Diffusion-Based Semantic Enhancer
PDF ↗Sign Language Translation (SLT) converts continuous sign videos into spoken language text, yet current models, whether gloss-based or gloss-free, struggle with long or discourse-level inputs. Recent architectures such as TwoStreamNetwork and CV-SLT have nearly saturated short-sentence accuracy, but their performance degrades on long sentences and multi-sentence paragraphs. In real scenarios such as news, interviews or daily conversations, signers naturally produce extended signing sequences with complex contextual dependencies. Moreover, identifying precise gloss boundaries remains a key obstacle, while gloss-based methods, though often superior, incur heavy annotation costs. The community therefore needs a solution that mitigates gloss dependency while preserving translation quality. We present BoostSLT, a context-aware framework for enhancing semantic consistency over long sign sequences without gloss supervision. Instead of requiring explicit gloss segmentation, BoostSLT introduces an Energy-Aware Temporal Segmentation (EAT-Seg) module that dynamically partitions videos into semantically coherent fragments, followed by a Diffusion-based Semantic Reconstruction (DSR) module that stitches and refines fragment-level translations into globally fluent paragraphs. The framework is plug-and-play and model-agnostic, seamlessly integrating with existing gloss-based or gloss-free pipelines across languages. Experiments on PHOENIX-2014T, CSL-Daily, and Auslan-Daily show consistent BLEU and ROUGE-L gains, confirming that diffusion-driven semantic reconstruction effectively bridges local accuracy and global coherence in long-form SLT. Code is available at github.com/K1sna/BoostSLT.
Vision-Language-Action (VLA) models have emerged as a powerful framework that unifies perception, language, and control, enabling robots to perform diverse tasks through multimodal understanding. However, current VLA models typically contain massive parameters and rely heavily on large-scale robot data pretraining, leading to high computational costs during training, as well as limited deployability for real-time inference.Moreover, most training paradigms often degrade the perceptual representations of the Vision-Language backbone, resulting in overfitting and poor generalization to downstream tasks.In this work, we present Evo-1, a lightweight VLA model that reduces computation and improves deployment efficiency, while maintaining strong performance without pretraining on robot data. Evo-1 builds on a native multimodal Vision-Language model (VLM), incorporating a novel cross-modulated diffusion transformer along with an optimized integration module, together forming an effective architecture.We further introduce a two-stage training paradigm that progressively aligns action with perception, preserving the representations of the VLM.Notably, with only 0.77 billion parameters, Evo-1 achieves state-of-the-art results on the Meta-World and RoboTwin suite, surpassing the previous best models by 12.4% and 6.9%, respectively, and also attains a competitive result of 94.8% on LIBERO.In real-world evaluations, Evo-1 attains a 78% success rate with high inference frequency and low memory overhead, outperforming all baseline methods.We release code, data, and model weights to facilitate future research on lightweight and efficient VLA models.
Prevailing 3D texture generation methods, which often rely on multi-view fusion, are frequently hindered by inter-view inconsistencies and incomplete coverage of complex surfaces, limiting the fidelity and completeness of the generated content. To overcome these challenges, we introduce TEXTRIX, a native 3D attribute generation framework for high-fidelity texture synthesis and downstream applications such as precise 3D part segmentation. Our approach constructs a latent 3D attribute grid and leverages a Diffusion Transformer equipped with sparse attention, enabling direct coloring of 3D models in volumetric space and fundamentally avoiding the limitations of multi-view fusion. Built upon this native representation, the framework naturally extends to high-precision 3D segmentation by training the same architecture to predict semantic attributes on the grid. Extensive experiments demonstrate state-of-the-art performance on both tasks, producing seamless, high-fidelity textures and accurate 3D part segmentation with precise boundaries.
Diffusion-based generative image compression has demonstrated remarkable potential for achieving realistic reconstruction at ultra-low bitrates. The key to unlocking this potential lies in making the entire compression process content-adaptive, ensuring that the encoder's representation and the decoder's generative prior are dynamically aligned with the semantic and structural characteristics of the input image. However, existing methods suffer from three critical limitations that prevent effective content adaptation. First, isotropic quantization applies a uniform quantization step, failing to adapt to the spatially varying complexity of image content and creating a misalignment with the diffusion model's noise-dependent prior. Second, the information concentration bottleneck---arising from the dimensional mismatch between the high-dimensional noisy latent and the diffusion decoder's fixed input---prevents the model from adaptively preserving essential semantic information in the primary channels. Third, existing textual conditioning strategies either need significant textual bitrate overhead or rely on generic, content-agnostic textual prompts, thereby failing to provide adaptive semantic guidance efficiently. To overcome these limitations, we propose a content-adaptive diffusion-based image codec (CADC) with three technical innovations: 1) an Uncertainty-Guided Adaptive Quantization (UGAQ) method that learns spatial uncertainty maps to adaptively align quantization distortion with content characteristics; 2) an Auxiliary Decoder-Guided Information Concentration (ADGIC) method that uses a lightweight auxiliary decoder to enforce content-aware information preservation in the primary latent channels; and 3) a Bitrate-Free Adaptive Textual Conditioning (BFATC) method that derives content-aware textual descriptions from the auxiliary reconstructed image, enabling semantic guidance without bitrate cost. Comprehensive experimental results show that our codec achieves state-of-the-art perceptual quality at ultra-low bitrates.
The primary axes of interest in low-light image enhancement (LLIE) are color constancy--ensuring consistent outputs across inputs of the same scene under varying illumination and noise--and generalization across diverse datasets. Existing methods, whether supervised, unsupervised, or zero-shot, rely on auxiliary loss functions and empirically selected hyperparameters, which yield strong results on the datasets used for evaluation but often exhibit limited generalization. To overcome these constraints, we propose MR. Illuminate (pronounced "Mister Illuminate"), the first deep learning-based solution for LLIE that requires no optimization and no degradation assumption. "MR." emphasizes our Modulate-Refine design: global illuminance and color are modulated via Adaptive Instance Normalization (AdaIN), while local structure and color are refined through self-attention features within a pre-trained diffusion model, taking a unique approach from prior methods. Extensive quantitative evaluations show that our approach surpasses SOTA methods on standard LLIE benchmarks, while qualitative results demonstrate improved color fidelity. Moreover, without any modification to our framework, our method achieves competitive results on the auto white balance (AWB) task, underscoring its strong generalization capability.
Training-free 3D editing aims to modify 3D shapes based on human instructions without model finetuning. It plays a crucial role in 3D content creation. However, existing approaches often struggle to produce strong or geometrically stable edits, largely due to inconsistent latent anchors introduced by timestep-dependent noise during diffusion sampling. To address these limitations, we introduce AnchorFlow, which is built upon the principle of latent anchor consistency. Specifically, AnchorFlow establishes a global latent anchor shared between the source and target trajectories, and enforces coherence using a relaxed anchor-alignment loss together with an anchor-aligned update rule. This design ensures that transformations remain stable and semantically faithful throughout the editing process. By stabilizing the latent reference space, AnchorFlow enables more pronounced semantic modifications. Moreover, AnchorFlow is mask-free. Without mask supervision, it effectively preserves geometric fidelity. Experiments on the Eval3DEdit benchmark show that AnchorFlow consistently delivers semantically aligned and structurally robust edits across diverse editing types. Project page: https://zhenglinzhou.github.io/AnchorFlow/.
From Inpainting to Layer Decomposition: Repurposing Generative Inpainting Models for Image Layer Decomposition
PDF ↗Images can be viewed as layered compositions, foreground objects over background, with potential occlusions. This layered representation enables independent editing of elements, offering greater flexibility for content creation. Despite the progress in large generative models, decomposing a single image into layers remains challenging due to limited methods and data. We observe a strong connection between layer decomposition and in/outpainting tasks, and propose adapting a diffusion-based inpainting model for layer decomposition using lightweight finetuning. To further preserve detail in the latent space, we introduce a novel multi-modality context fusion module with linear attention complexity. Our model is trained purely on a synthetic dataset constructed from open-source assets and achieves superior performance in object removal and occlusion recovery, unlocking new possibilities in downstream editing and creative applications.
Audio-visual deepfake localization demands interval-level outputs that serve as temporal evidence. Despite recent progress, symmetric fusion under single-sided or asynchronous forgeries propagates cross-modal noise, degrading high-precision localization. We present IaMSB, an inconsistency-aware multimodal Schrodinger Bridge (SB) that jointly estimates cross-modal consistency and performs interval-level localization. Unlike diffusion models, SB minimizes path-distribution discrepancy and yields consistency scores without explicit noise injection or denoising. With the Schrodinger Bridge (SB), IaMSB unifies consistency estimation, cross-modal information selection, and bridge-step scheduling in one framework. Specifically, a lightweight coarse bridge first proposes candidate intervals and estimates cross-modal consistency; these statistics select cross-modal witness signals and allocate bridge steps asymmetrically across modalities. A refinement bridge then performs step-tuned fusion and outputs refined, time-aligned intervals. IaMSB anticipates single-sided and asynchronous forgeries and, using bottlenecked cross-modal interaction with step allocation, suppresses noise transfer, avoids unnecessary iterations. Across benchmarks, IaMSB stabilizes strict-IoU boundary precision, raising AP@0.95 by 3~10%, and yields improved high-precision localization, particularly for single-sided forgeries.
We introduce OneCAT, a unified multimodal model that seamlessly integrates understanding, generation, and editing within a single decoder-only transformer architecture. OneCAT uniquely eliminates the need for external components such as Vision Transformers (ViT) or vision tokenizer during inference, leading to significant efficiency gains, especially for high-resolution image inputs and outputs. This is achieved through a modality-specific Mixture-of-Experts (MoE) design trained with a unified autoregressive (AR) objective, which also natively supports dynamic resolutions. Furthermore, we pioneer to achieve multi-scale visual autoregressive mechanism within the Large Language Model (LLM) with proposed scale-aware adapter (SAA) that drastically reduces decoding latency compared to diffusion-based methods while maintaining state-of-the-art performance. Our findings demonstrate the powerful potential of pure autoregressive modeling as an elegant foundation for unified multimodal intelligence. As a result, OneCAT outperforms existing unified models across benchmarks for multimodal understanding, generation, and editing.
The proliferation of highly realistic AI-generated images poses critical challenges for digital forensics, demanding precise pixel-level localization of manipulated regions. Existing methods predominantly learn discriminative patterns of specific forgeries, struggling with novel manipulations as editing techniques evolve. We propose the Iterative Forgery Amplifier Network (IFA-Net), which shifts from learning "what is fake" to modeling "what is real". Grounded in the principle that all manipulations deviate from the natural image manifold, IFA-Net leverages a frozen Masked Autoencoder (MAE) pretrained on real images as a universal realness prior. Our framework operates through a two-stage closed-loop process: an initial Dual-Stream Segmentation Network (DSSN) fuses the original image with MAE reconstruction residuals for coarse localization, then a Task-Adaptive Prior Injection (TAPI) module converts this coarse prediction into guiding prompts that steer the MAE decoder to amplify reconstruction failures in suspicious regions, enabling precise refinement. Extensive experiments on four diffusion-based inpainting benchmarks show that IFA-Net achieves an average improvement of 6.5% in IoU and 8.1% in F1-score over the second-best method, while demonstrating strong generalization to traditional manipulation types.
The rapid advancement of diffusion-based image generation models has raised serious concerns regarding potential copyright and privacy infringements involving human-created data. Membership inference attacks (MIAs) have emerged as a promising tool for identifying unauthorized data usage during model training. Existing methods typically assess the ability of model to denoise perturbed suspect images as an indicator of membership status. However, the discriminative power of such features is highly dependent on the degree of model memorization and deteriorates significantly when applied to less exposed data (e.g., pre-training data). Although several methods attempt to enhance detection by leveraging internal model features, these features are generally inaccessible in mainstream closed-source image generation platforms, limiting their practicality. In this paper, we demonstrate that analyzing how a black-box diffusion model denoises a target image and corresponding perturbed textual instructions can reveal more distinctive membership cues. Based on this insight, we propose a black-box membership inference attack framework (named SD-MIA) that leverages a cross-modal data perturbation mechanism to detect pre-training data in diffusion models. We conduct extensive experiments on both a public benchmark dataset and a newly constructed dataset, each comprising pre-training membership and non-membership samples with identical distributions. Experimental results demonstrate that SD-MIA achieves superior performance compared to existing baselines, including those with the unfair advantage of accessing internal model features.
Generating long videos using pre-trained video diffusion models, which are typically trained on short clips, presents a significant challenge. Directly applying these models for long-video inference often leads to a notable degradation in visual quality. This paper identifies that this issue primarily stems from two out-of-distribution (O.O.D) problems: frame-level relative position O.O.D and contextlength O.O.D. To address these challenges, we propose FreeLOC, a novel training-free, layer-adaptive framework that introduces two core techniques: Video-based Relative Position Re-encoding (VRPR) for frame-level relative position O.O.D, a multi-granularity strategy that hierarchically re-encodes temporal relative positions to align with the model's pre-trained distribution, and Tiered Sparse Attention (TSA) for context-length O.O.D, which preserves both local detail and long-range dependencies by structuring attention density across different temporal scales. Crucially, we introduce a layer-adaptive probing mechanism that identifies the sensitivity of each transformer layer to these O.O.D issues, allowing for the selective and efficient application of our methods. Extensive experiments demonstrate that our approach significantly outperforms existing training-free methods, achieving state-of-the-art results in both temporal consistency and visual quality. Code is available at https://github.com/Westlake-AGI-Lab/FreeLOC.
Radiance field methods (e.g. 3D Gaussian Splatting) have emerged as a powerful paradigm for novel view synthesis, yet their appearance modeling often relies on Spherical Harmonics (SH), which impose fundamental limitations. SH struggle with high-frequency signals, exhibit Gibbs ringing artifacts, and fail to capture specular reflections---a key component of realistic rendering. Although alternatives like spherical Gaussians offer improvements, they add significant optimization complexity. We propose Spherical Voronoi (SV) as a unified framework for appearance representation in 3D Gaussian Splatting. SV partitions the directional domain into learnable regions with smooth boundaries, providing an intuitive and stable parameterization for view-dependent effects. For diffuse appearance, SV achieves competitive results while keeping optimization simpler than existing alternatives. For reflections---where SH fail---we leverage SV as learnable reflection probes, taking reflected directions as input following principles from classical graphics. This formulation attains state-of-the-art results on synthetic and real-world datasets, demonstrating that SV offers a principled, efficient, and general solution for appearance modeling in explicit 3D representations.
D$^2$-FOSA: Dual-Diffusion Guided EEG-to-Image Reconstruction with Frequency-Oriented Semantic Alignment
PDF ↗Reconstructing visual semantics from Electroencephalography (EEG) signals enables a deeper understanding of human visual cognition and supports next-generation brain-computer interface (BCI) applications.Despite notable advances in recent years, most existing EEG encoders still struggle to capture the frequency-specific neural dynamics that reflect perceptual and cognitive rhythms. Moreover, the cross-modal alignment between EEG and visual content remains insufficiently tackled, leading to limited semantic consistency and visual fidelity. To address these issues, we propose D^2-FOSA, a unified dual-diffusion guided framework with frequency-oriented semantic alignment, which strengthens the frequency-aware EEG representation for more semantically aligned image reconstruction.Specifically, we design a Frequency-Spatio-Temporal Dynamics Encoder (FSTDE) based on the Frequency-Oriented Mamba (FOMamba) to explicitly model oscillatory patterns and long-range dependencies in EEG signals. The extracted features are then pulled into the CLIP-aligned visual semantic space via contrastive learning.Meanwhile, a Dual Diffusion Latent Generator (DDLG) with bidirectional EEG-image conditioning is designed to enforce cross-modal alignment and promote cycle-consistent generation.Extensive experiments on four challenging datasets demonstrate that our proposed D^2-FOSA significantly outperforms existing methods in both retrieval and reconstruction tasks. Particularly, our D^2-FOSA surpasses the contemporary MB2C method by over 17 FID in the reconstruction task on THINGS-EEG, indicating a substantial improvement in perceptual fidelity. The source code is in the supplementary material.
The diffusion model presents a powerful ability to capture the entire (conditional) data distribution. However, due to the lack of sufficient training and data to learn to cover low-probability areas, the model will be penalized for failing to generate high-quality images corresponding to these areas. To achieve better generation quality, guidance strategies such as classifier free guidance (CFG) can guide the samples to the high-probability areas during the sampling stage. However, the standard CFG often leads to over-simplified or distorted samples. On the other hand, the alternative line of guiding diffusion model with its bad version is limited by carefully designed degradation strategies, extra training and additional sampling steps. In this paper, we proposed a simple yet effective strategy Internal Guidance (IG), which introduces an auxiliary supervision on the intermediate layer during training process and extrapolates the intermediate and deep layer's outputs to obtain generative results during sampling process. This simple strategy yields significant improvements in both training efficiency and generation quality on various baselines. On ImageNet 256x256, SiT-XL/2+IG achieves FID=5.31 and FID=1.75 at 80 and 800 epochs. More impressively, LightningDiT-XL/1+IG achieves FID=1.34 which achieves a large margin between all of these methods. Combined with CFG, LightningDiT-XL/1+IG achieves the current state-of-the-art FID of 1.19.