CVPR 2026 论文列表

Text-to-image (T2I) diffusion models currently lack an efficient mechanism for early quality assessment, forcing costly random trial-and-error in scenarios requiring multiple generations (e.g., iterating on prompts, agent-based image generation, flow-grpo). To address this, we first reveal a strong correlation between the attention distribution in the early diffusion process and the final image quality. Building upon this insight, we introduce Diffusion Probe, a pioneering framework that leverages the model's internal cross-attention maps as a predictive signal. We propose a lightweight predictor, trained to establish a direct mapping from statistical properties of these nascent cross-attention distributions--extracted from the initial denoising steps--to the final image's comprehensive quality. This allows our probe to accurately forecast various aspects of image quality, regardless of the specific ground-truth quality metric, long before full synthesis is complete. We empirically validate the reliability and generalizability of Diffusion Probe through its consistently strong predictive accuracy across a wide spectrum of conditions. On diverse T2I models (e.g., SDXL, FLUX, Qwen-Image), throughout broad early-denoising windows, across various resolutions, and with different quality metrics, it achieves high correlation (PCC > 0.7) and classification performance (AUC-ROC > 0.9). This intrinsic reliability is further demonstrated in practice by successfully optimizing T2I workflows that benefit from early, quality-guided decisions, such as Prompt Optimization, Seed Selection, and Accelerated RL Training. In these applications, the probe's early signal enables more targeted sampling strategies, preempting costly computations on low-potential paths. This yields a dual benefit: a significant reduction in computational overhead and a simultaneous improvement in final outcome quality, establishing Diffusion Probe as a model-agnostic and broadly applicable tool poised to revolutionize T2I efficiency.

Accurately anticipating how complex, diverse scenes will evolve requires models that represent uncertainty, simulate along extended interaction chains, and efficiently explore many plausible futures. Yet most existing approaches rely on dense video or latent-space prediction, expending substantial capacity on dense appearance rather than on the underlying sparse trajectories of points in the scene. This makes large-scale exploration of future hypotheses costly and limits performance when long-horizon, multi-modal motion is essential. We address this by formulating the prediction of open-set future scene dynamics as step-wise inference over sparse point trajectories. Our autoregressive diffusion model advances these trajectories through short, locally predictable transitions, explicitly modeling the growth of uncertainty over time. This dynamics-centric representation enables fast rollout of thousands of diverse futures from a single image, optionally guided by initial constraints on motion, while maintaining physical plausibility and long-range coherence. We further introduce OWM, a benchmark for open-set motion prediction based on diverse in-the-wild videos, to evaluate accuracy and variability of predicted trajectory distributions under real-world uncertainty. Our method matches or surpasses dense simulators in predictive accuracy while achieving orders-of-magnitude higher sampling speed, making open-set future prediction both scalable and practical.

Diagrams are widely used in daily life. However, offline diagrams typically exist in the form of images, lacking structured data representation, which significantly limits their reusability and editability. Current research mainly focuses on supporting basic query tasks for online diagrams and does not meet the semantic understanding and interaction requirements for complex offline diagrams. Although large language models (LLMs) possess powerful reasoning and knowledge integration capabilities, their performance in processing offline diagrams is unsatisfactory due to the inability to accurately understand the structure and content of offline diagrams. To address these issues,we propose DiagramDiff, a framework consisting of a high-precision diagram reconstruction model and an instance-level diagram element recognition model. The framework converts offline diagrams into standardized data structures, enabling LLMs to transition from being unable to understand offline diagrams to becoming intelligent assistants capable of performing tasks such as semantic reasoning, logical validation, and efficient diagram editing. We have constructed a dataset containing diagrams and their corresponding question and answering(Q&A) and editing tasks. Experiments demonstrate that DiagramDiff achieves state-of-the-art performance in diagram reconstruction and recognition tasks, significantly enhancing LLMs' understanding and interaction capabilities with offline diagrams.

We introduce Illustrator's Depth, a novel definition of depth that addresses a key challenge in digital content creation: decomposing flat images into editable, ordered layers. Inspired by an artist's compositional process, illustrator's depth infers a layer index for each pixel, forming an interpretable image decomposition through a discrete, globally consistent ordering of elements optimized for editability. We also propose and train a neural network using a curated dataset of layered vector graphics to predict layering directly from raster inputs. Our layer index inference unlocks a range of powerful downstream applications. In particular, it significantly outperforms state-of-the-art baselines for image vectorization while also enabling high-fidelity text-to-vector-graphics generation, automatic 3D relief generation from 2D images, and intuitive depth-aware editing. By reframing depth from a physical quantity to a creative abstraction, illustrator's depth prediction offers a new foundation for editable image decomposition.

Spatial transcriptomics (ST) measures gene expression at fine-grained spatial resolution, offering insights into tissue molecular landscapes. Previous methods for spatial gene expression prediction typically crop spots of interest from histopathology slide images, and train models to map each spot to a corresponding gene expression profile. However, these methods inherently lose the spatial resolution in gene expression:1) each spot often contains multiple cells with distinct gene expression profiles;2) spots are typically defined at fixed spatial resolutions, limiting the ability to predict gene expression at varying scales. To address these limitations, this paper presents PixNet, a dense prediction network capable of predicting spatially resolved gene expression across spots of varying sizes and scales directly from histopathology slide images. Different from previous methods that map individual spots to gene expression values, we generate a spatially dense continuous gene expression map from the histopathology slide image, and aggregate values within spots of interest to predict the gene expression. Our PixNet outperforms state-of-the-art methods on four common ST datasets in multiple spatial scales. The source code is available at: https://github.com/wangzrk/From-Spots-to-Pixels.

Distinct from attention-based compression methods, this paper presents an information uniqueness driven video compression framework, termed UniComp, which aims to maximize the information fidelity of video representations under constrained computational budgets. Starting from the information-theoretic perspective, we formulate the vision compression as an optimization problem that minimizes conditional entropy (reconstruction error) between retained and full tokens. To achieve this, we introduce the notion of information uniqueness to measure intrinsic redundancy among tokens to link with reconstruction error. Based on uniqueness, we design three modules--Frame Group Fusion, Token Allocation, and Spatial Dynamic Compression--that progressively perform semantic frame grouping, adaptive resource allocation, and fine-grained spatial compression. Extensive experiments demonstrate that UniComp consistently outperforms existing compression methods in preserving essential visual tokens under limited computational budgets, highlighting the pivotal role of information uniqueness in token compression efficacy.