CVPR 2026 Papers

We present YOEO, an approach for object erasure. Unlike recent diffusion-based methods which struggle to erase target objects without generating unexpected content within the masked regions due to lack of sufficient paired training data and explicit constraint on content generation, our method allows to produce high-quality object erasure results free of unwanted objects or artifacts while faithfully preserving the overall context coherence to the surrounding content. We achieve this goal by training an object erasure diffusion model on unpaired data containing only large-scale real-world images, under the supervision of a sundries detector and a context coherence loss that are built upon an entity segmentation model. To enable more efficient training and inference, a diffusion distillation strategy is employed to train for a few-step erasure diffusion model. Extensive experiments show that our method outperforms the state-of-the-art object erasure methods. Our code is available at https://zyxunh.github.io/YOEO-ProjectPage/.

Recent progress in 3D reconstruction has made it easy to create realistic digital twins from everyday environments. However, current digital twins remain largely static--limited to navigation and view synthesis without embodied interactivity. To bridge this gap, we introduce Dexterous World Model (DWM), an scene-action-conditioned video diffusion model enabling embodied interaction within static 3D scenes. Given a static 3D scene rendering and an egocentric hand motion sequence, DWM generates temporally coherent videos depicting plausible human-scene interactions. Our approach conditions video generation on (1) static scene renderings following a specified camera trajectory to ensure spatial consistency, and (2) egocentric hand mesh renderings that encode both geometry and motion cues in the egocentric view to model action-conditioned dynamics directly. We train our model on a synthetic human-scene interaction dataset and real-world object manipulation dataset, then evaluate it across both synthetic and real-world egocentric benchmarks. Experiments demonstrate that DWM enables realistic, physically grounded interactions, such as grasping, opening, or moving objects, while maintaining camera and scene consistency. This framework establishes the first step toward video diffusion-based interactive digital twins, enabling embodied simulation and 3D scene interactivity from egocentric actions.

Diffusion-based image compression has recently shown outstanding perceptual fidelity, yet its practicality is hindered by prohibitive sampling overhead and high memory usage.Most existing diffusion codecs employ UNet architectures, where hierarchical downsampling forces diffusion to operate in shallow latent spaces (typically with only 8xspatial downscaling), resulting in excessive computation.In contrast, conventional VAE-based codecs work in much deeper latent domains (16x-64xdownscaled), motivating a key question:Can diffusion operate effectively in such compact latent spaces without compromising reconstruction quality?To address this, we introduce DiT-IC--an Aligned Diffusion Transformer for Image Compression--which replaces the UNet with a Diffusion Transformer capable of performing diffusion in latent space entirely at 32xdownscaled resolution.DiT-IC adapts a pretrained text-to-image multi-step DiT into a single-step reconstruction model through three key alignment mechanisms:(1) a variance-guided reconstruction flow that adapts denoising strength to latent uncertainty for efficient reconstruction;(2) a self-distillation alignment that enforces consistency with encoder-defined latent geometry to enable one-step diffusion; and(3) a latent-conditioned guidance that replaces text prompts with semantically aligned latent conditions, enabling text-free inference.With these designs, DiT-IC achieves state-of-the-art perceptual quality while offering up to 30x faster decoding and drastically lower memory usage than existing diffusion-based codecs. Remarkably, it can reconstruct 2048x2048 images on a 16 GB laptop GPU.

Diffusion models have emerged as the leading approach for text-to-image generation. However, their iterative sampling process, which gradually morphs random noise into coherent images, introduces significant latency that limits their applicability. While recent few-step diffusion models reduce the number of sampling steps to as few as one to four steps, they often compromise image quality and prompt alignment, especially in one-step generation. Additionally, these models require computationally expensive training procedures. To address these limitations, we propose ImageRAGTurbo, a novel approach to efficiently finetune few-step diffusion models via retrieval augmentation. Given a text prompt, we retrieve relevant text-image pairs from a database and use them to condition the generation process. We argue that such retrieved examples provide rich contextual information to the UNet denoiser that helps reduce the number of denoising steps without compromising image quality. Indeed, our initial investigations show that using the retrieved content to edit the denoiser's latent space (\mathcal H -space) without additional finetuning already improves prompt fidelity. To further improve the quality of the generated images, we augment the UNet denoiser with a trainable adapter in the \mathcal H -space, which efficiently blends the retrieved content with the target prompt using a cross-attention mechanism. Experimental results on fast text-to-image generation demonstrate that our approach produces high-fidelity images without compromising latency compared to existing methods.

Text-driven video editing aims to modify video content based on natural language instructions. While recent training-free methods have leveraged pretrained diffusion models, they often rely on an inversion-editing paradigm. This paradigm maps the video to a latent space before editing. However, the inversion process is not perfectly accurate, often compromising appearance fidelity and motion consistency. To address this, we introduce FlowDirector, a novel training-free and inversion-free video editing framework. Our framework models the editing process as a direct evolution in the data space. It guides the video to transition smoothly along its inherent spatio-temporal manifold using an ordinary differential equation (ODE), thereby avoiding the inaccurate inversion step. From this foundation, we introduce three flow correction strategies for appearance, motion, and stability: 1) Direction-aware flow correction amplifies components that oppose the source direction and removes irrelevant terms, breaking conservative streamlines and enabling stronger structural and textural changes. 2) Motion-appearance decoupling optimizes motion agreement as an energy term at each timestep, significantly improving consistency and motion transfer. 3) Differential averaging guidance strategy leverages differences among multiple candidate flows to approximate a low variance regime at low cost, suppressing artifacts and stabilizing the trajectory. Extensive experiments across various editing tasks and benchmarks demonstrate that FlowDirector achieves state-of-the-art performance in instruction following, temporal consistency, and background preservation, establishing an efficient new paradigm for coherent video editing without inversion.

Human video generation has advanced rapidly with the development of diffusion models, but the high computational cost and substantial memory consumption associated with training these models on high-resolution, multi-frame data pose significant challenges. In this paper, we propose Entropy-Guided Prioritized Progressive Learning (Ent-Prog), an efficient training framework tailored for diffusion models on human video generation. First, we introduce Conditional Entropy Inflation (CEI) to assess the importance of different model components on the target conditional generation task, enabling prioritized training of the most critical components. Second, we introduce an adaptive progressive schedule that adaptively increases computational complexity during training by measuring the convergence efficiency. Ent-Prog reduces both training time and GPU memory consumption while maintaining model performance. Extensive experiments across three datasets, demonstrate the effectiveness of Ent-Prog, achieving up to 2.2x training speedup and 2.4x GPU memory reduction without compromising generative performance.

We present an approach for high-quality dynamic Gaussian Splatting from monocular videos. To this end, we in this work go one step further beyond previous methods to explicitly model continuous position and orientation deformation of dynamic Gaussians, using an SE(3) B-spline motion bases with a compact set of control points. To improve computational efficiency while enhancing the ability to model complex motions, an adaptive control mechanism is devised to dynamically adjust the number of motion bases and control points. Besides, we develop a soft segment reconstruction strategy to mitigate long-interval motion interference, and employ a multi-view diffusion model to provide multi-view cues for avoiding overfitting to training views. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in novel view synthesis. Our code is available at https://github.com/hhhddddddd/se3bsplinegs.

Instructional video editing applies edits to an input video using only text prompts, enabling intuitive natural-language control. Despite the rapid progress, most methods still require fixed-length inputs and substantial compute. Meanwhile, autoregressive video generation enables efficient variable-length synthesis, yet remains under-explored for video editing. We introduce a causal, efficient video editing model that edits variable-length videos frame by frame. For efficiency, we start from a 2D image-to-image (I2I) diffusion model and adapt it to video-to-video (V2V) editing by conditioning the edit at time step t on the model's prediction at t-1. To leverage videos' temporal redundancy, we propose a new I2I diffusion forward process formulation that encourages the model to predict the residual between the target output and the previous prediction. We call this \underline R esidual \underline F low \underline D iffusion \underline M odel (\methodname), which focuses the denoising process on changes between consecutive frames. Moreover, we propose a new benchmark that better ranks state-of-the-art methods by faithfulness for video editing tasks. Trained on paired video data for global/local style transfer and object removal, \methodname surpasses I2I-based methods and competes with fully spatiotemporal (3D) V2V models, while matching the compute of image models and scaling independently of input video length. More content can be found in \href https://smsd75.github.io/RFDM_page/ RFDM page .

Video Super-Resolution (VSR) fundamentally struggles with a critical trade-off: single-step models offer unmatched efficiency but often lack the high-frequency detail, creativity, and visual quality of their multi-step diffusion counterparts, which are computationally prohibitive for practical use. In this paper, we propose PS-SR, a novel "pseudo" single-step VSR framework that transcends this trade-off through a computationally asymmetric sampling pipeline. The key to PS-SR lies in its speculative diffusion mechanism: a powerful base model performs only a single, comprehensive sampling step, establishing the global structure and content fidelity, after which a lightweight draft model, directly augmented by the base model's features, speculatively performs subsequent refinements. Crucially, we further enforce a frequency-domain update rule that constrains these refinements to exclusively inject high-frequency details, preserving the foundational low-frequency content and preventing semantic drift across sampling steps. By doing so, PS-SR creates the "illusion" of a single-step model--delivering the similar inference speeds and input-output content consistency--while achieving the visual richness and creativity typically reserved for costly multi-step generative models. We demonstrate that our "pseudo-single-step" paradigm achieves state-of-the-art quality with a comparable speed to single-step models, paving the way for real-time, high-fidelity video enhancement. Please refer to our project page for more results: https://waq2001.github.io/PS-SR-page/.

Diffusion-based virtual try-on methods rely on vast high-quality garment-person pairs, which are scarce in practice due to the high cost of data collection and preprocessing, limiting their performance in real-world scenarios.To overcome this bottleneck, we propose Cycle-Consistent Virtual Try-On (CCVTON), a diffusion-based approach that enables effective training using massive in-the-wild person images. Specifically, CCVTON introduces a Cycle-Consistent Learning (CCL) strategy that just employs a single unified generative model to disentangle a garment from a person image (try-off branch) and transfer it to the same individual (try-on branch), forming a reconstruction cycle. To this end, we first warm up a Unified Diffusion Transformer (UDiT) on open-source paired data to acquire basic try-on and try-off capabilities. When adapting UDiT to in-the-wild person images, we employ a Multi-Criteria Filtering Operation to select high-quality garments disentangled from person images by the pretrained UDiT. These filtered garments are not used as inputs for CCL, but serve as soft constraints for a perceptual regularization loss, preventing the try-off branch from collapsing to trivial copying. In addition, we propose garment-aware mask generation with a two-stage refinement process to suppress garment leakage while maintaining person consistency. Extensive experiments show that CCVTON achieves state-of-the-art results.

Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Yet in the video generation domain, where spatiotemporal complexity and computational cost are substantially higher, state-of-the-art systems almost exclusively rely on diffusion-based models. In this work, we revisit this design space by presenting STARFlow-V, a normalizing flow-based video generator with substantial benefits such as end-to-end learning, robust causal prediction and native likelihood estimation. Building upon the recently proposed STARFlow, STARFlow-V operates in the spatiotemporal latent space with a global-local architecture which restricts causal dependencies to a global latent space while preserving rich local within-frame interactions. This eases error accumulation over time, a common pitfall of standard autoregressive diffusion model generation. Additionally, we propose flow-score matching, which equips the model with a light-weight causal denoiser to improve the video generation consistency in an autoregressive fashion. To improve the sampling efficiency, STARFlow-V employs a video-aware Jacobi iteration scheme that recasts inner updates as parallelizable iterations without breaking causality. Thanks to the invertible structure, the same model can natively support text-to-video, image-to-video as well as video-to-video generation tasks. Empirically, STARFlow-V achieves strong visual fidelity and temporal consistency with practical sampling throughput relative to diffusion-based baselines. These results present the first evidence, to our knowledge, that NFs are capable of high-quality autoregressive video generation, establishing them as a promising research direction for building world models.

Single-image relighting is highly under-constrained: small illumination changes can produce large, nonlinear variations in shading, shadows, and specularities, while geometry and materials remain unobserved. Existing diffusion-based approaches either rely on intrinsic- or G-buffer-based pipelines that require dense and fragile supervision, or operate purely in latent space without physical grounding, making fine-grained control of direction, intensity, and color unreliable. We observe that full intrinsic decomposition is unnecessary for accurate relighting. Instead, sparse but physically meaningful cues--indicating where illumination should change and how materials should respond--are sufficient to guide a diffusion model. Based on this insight, we introduce LightCtrl that integrates minimal physical priors at two levels: a few-shot latent proxy encoder that extracts compact material-geometry cues from limited PBR supervision, and a lighting-aware mask that identifies illumination-sensitive regions and steers the denoiser toward shading-relevant pixels. To compensate for scarce PBR data, we refine the proxy branch using a DPO-based objective that aligns predicted cues with perceptually preferred relighting behavior. We further present ScaLight, a large-scale object-level dataset with systematically varied illumination and complete camera-light metadata, enabling physically consistent and controllable training. Across object- and scene-level benchmarks, our method achieves photometrically faithful relighting with accurate continuous control, surpassing prior diffusion- and intrinsic-based baselines, including gains of up to +2.4 dB PSNR and 35% lower RMSE under controlled lighting shifts.

Recent generative models can create visually plausible 3D representations of objects. However, the generation process often allows for implicit control signals, such as contextual descriptions, and rarely supports bold geometric distortions beyond existing data distributions. We propose a geometric stylization framework that deforms a 3D mesh, allowing it to express the style of an image. While style is inherently ambiguous, we utilize pre-trained diffusion models to extract an abstract representation of the provided image. Our coarse-to-fine stylization pipeline can drastically deform the input 3D model to express a diverse range of geometric variations while retaining the valid topology of the original mesh and part-level semantics. We also propose an approximate VAE encoder that provides efficient and reliable gradients from mesh renderings. Extensive experiments demonstrate that our method can create stylized 3D meshes that reflect unique geometric features of the pictured assets, such as expressive poses and silhouettes, thereby supporting the creation of distinctive artistic 3D creations. Project page: https://changwoonchoi.github.io/GeoStyle

Denoising in the sRGB image space is challenging due to large noise variability. Although end-to-end methods perform well, their effectiveness in real-world scenarios is limited by the scarcity of real noisy-clean image pairs, which are expensive and difficult to collect. To address this limitation, several generative methods have been developed to synthesize realistic noisy images from limited data. These approaches often rely on camera metadata during both training and testing to synthesize real-world noise. However, the lack of metadata or inconsistencies between devices restricts their usability. Therefore, we propose a novel framework called Prompt-Driven Noise Generation (PNG). This model is capable of acquiring high-dimensional prompt features that capture the characteristics of real-world input noise and creating a variety of realistic noisy images consistent with the distribution of the input noise. By eliminating the dependency on explicit camera metadata, our approach significantly enhances the generalizability and applicability of noise synthesis. Comprehensive experiments reveal that our model effectively produces realistic noisy images and show the successful application of these generated images in removing real-world noise across various benchmark datasets.

Latent generative modeling has emerged as the dominant paradigm for Diffusion Transformers (DiT), where a pretrained autoencoder compresses image pixels into a latent space to facilitate the diffusion process. Recently, the use of semantic encoders within autoencoders (AEs) has gained attention, yet their influence on image reconstruction and diffusion model training remains insufficiently explored. In this study, we perform an in-depth examination of how semantic encoders shape latent representation learning for the autoencoders. Our findings reveal a fundamental trade-off: while semantic encoders generate latent spaces enriched with visual semantics, their high level of abstraction makes it challenging to capture fine-grained geometric relationships, thereby requiring larger models and longer training for convergence. To address this issue, we build upon recent advances in representation learning that enable the joint modeling of both semantic abstraction and geometric detail. This leads to a Semantic Auto-Encoder (S-AE) that achieves state-of-the-art performance, combining superior reconstruction quality and discriminative capability. Specifically, with S-AE, we are able to provide a unified latent space that achieves 0.06 FID for image reconstruction and 81.9% classification accuracy on ImageNet, set a state-of-the-art benchmark. Codes and model weights will be made publicably available.

Realistic 3D city generation is fundamental to a wide range of applications, including virtual reality and digital twins. However, most existing methods rely on training a single diffusion model, which limits their ability to generate personalized and boundless city-scale scenes. In this paper, we present Yo'City, a novel agentic framework that enables user-customized and infinitely expandable 3D city generation by leveraging the reasoning and compositional capabilities of off-the-shelf large models. Specifically, Yo'City first conceptualizes the city through a top-down planning strategy that defines a hierarchical "City-District-Grid" structure. The Global Planner determines the overall layout and potential functional districts, while the Local Designer further refines each district with detailed grid-level descriptions. Subsequently, the grid-level 3D generation is achieved through a produce-refine-evaluate isometric image synthesis loop, followed by image-to-3D generation. To simulate continuous city evolution, Yo'City further introduces a user-interactive, relationship-guided expansion mechanism, which performs scene graph-based distance- and semantics-aware layout optimization, ensuring spatially coherent city growth. To comprehensively evaluate our method, we construct a diverse benchmark dataset and design six multi-dimensional metrics that assess generation quality from the perspectives of semantics, geometry, texture, and layout. Extensive experiments demonstrate that Yo'City consistently outperforms existing state-of-the-art methods across all evaluation aspects.

Establishing accurate correspondence between sparse line representations and rich textured imagery remains a formidable challenge. While diffusion features excel in semantic correspondence, they struggle to bridge the fundamental gap between abstract sketches and texture-rich photographs. We identify two critical disparities: spatial domain misalignment from structural abstraction differences, and frequency domain inconsistencies from texture density variations. Based on this analysis, we propose SFA-DIFT, a novel approach that learns spatial-frequency aligned diffusion features for robust cross-modal correspondence. Unlike previous methods focusing solely on spatial alignment, our key innovation performs dual-domain alignment by learning unified clean diffusion features while strategically aggregating low-frequency components in the frequency domain. This comprehensive spatial-frequency alignment enables equitable understanding between sparse abstractions and rich textures. To validate our approach, we extend the existing sketch-photo correspondence dataset (PSC6K) by generating multi-style textured imagery, creating MS-PSC6K, a comprehensive correspondence benchmark. Extensive experiments demonstrate that SFA-DIFT achieves state-of-the-art performance, delivering substantial improvements with an average of 0.87% on PCK@1, 2.20% on PCK@5, and 0.95% on PCK@10 over previous best methods, validating the effectiveness and robustness of our dual-domain alignment approach. Codes are available on https://github.com/Mofr77/SFA-DIFT.

Reconstructing photorealistic and animatable 4D head avatars from a single portrait image remains a fundamental challenge in computer vision. While diffusion models have enabled remarkable progress in image and video generation for avatar reconstruction, existing methods primarily rely on 2D priors and struggle to achieve consistent 3D geometry. We propose a novel framework that leverages geometry-aware diffusion to distill strong geometry priors for high-fidelity head avatar reconstruction. Our approach jointly synthesizes portrait images and corresponding surface normals, while a pose-free expression encoder captures implicit expression representations. Both synthesized images and expression latents are distilled into 3D Gaussian-based avatars, enabling photorealistic rendering with accurate geometry. Extensive experiments demonstrate that our method substantially outperforms state-of-the-art approaches in visual quality, expression fidelity, and cross-identity generalization, while supporting real-time rendering.

Nighttime photography is susceptible to flare caused by strong light sources, which degrades visual quality and disrupts structural information required by downstream vision tasks. Existing nighttime flare removal methods generally lack semantic priors for flare-occluded regions and thus tend to introduce artifacts and lose details under severe degradation. To address this problem, we propose a language-guided one-step diffusion framework that explicitly aligns flare-occluded regions with the underlying scene content at the semantic level. Specifically, we develop the first flare-specific vision-language model, Flare-VLM, which extracts fine-grained textual descriptions to guide one-step diffusion for high-quality restoration of severely damaged areas. Then, we propose semantics-aware distribution distillation to constrain the noise distribution with high-level semantics, suppressing redundant perturbations on clean backgrounds and improving the stability of distillation. In addition, we design an instruction-driven data synthesis pipeline to generate geometrically and semantically aligned nighttime flare samples, narrowing the gap to real degradations. Experimental results demonstrate that the proposed method achieves better restoration and enhances the performance of downstream vision tasks.