Existing Blind Super-Resolution (BSR) methods are mostly trained on artificial synthetic degradation data pairs or rely on specific degradation priors, which lead to poor performance due to the trained degradation mismatch between other unknown complex degradations in real-world scenarios. To tackle this problem, we propose a novel Diffusion-based Disentangled Degradation representation method for BSR, dubbed D3BSR, which disentangles arbitrary unknown degradation into structure and texture degradations to enhance perception and fidelity quality individually. Specifically, the structure degradation is optimized by degradation distribution transition with a self-supervised collaborative learning strategy to recursively minimize the perception error. The texture degradation is restored through posterior sampling controlled by a fidelity coefficient to leverage rich texture priors encapsulated in a pre-trained diffusion model for preserving fidelity. The degraded image is super-resolved using an analytical solution with the pseudo inverse of the structural and texture degradation, which achieves a controllable trade-off between perception and fidelity and does not rely on any degradation priors or extra-supervised training. Extensive experiments on the nine heavily degraded synthetic and real-world natural and face datasets demonstrate that our D3BSR outperforms SOTA methods on the diverse metrics in reconstruction faithfulness and perceptual quality.

Remote sensing image fusion aims to reconstruct a high spatial and spectral resolution image by integrating the spatial and spectral information from multiple remote sensing sensor data. Despite the remarkable progress of deep learning-based fusion methods, most existing methods rely on manual network architecture design and hyperparameter tuning, lacking sufficient interpretability and adaptability. To address this limitation, we propose a novel neural Ordinary Differential Equation (ODE)-inspired tuning-free proximal splitting algorithm, which splits remote sensing image fusion as two optimization problems regularized by deep priors to model the fusion of spatial and spectral. Firstly, based on the physical properties of spatial and spectral information, the two problems are optimized by two proximal splitting operators to iteratively integrate spatial-spectral complementary information, eliminating or suppressing redundant information to reduce fusion errors. Secondly, considering the efficiency of neural ODE in reducing optimization error, we utilize a high-order numerical scheme to customize the proximal operator theoretically without additional handcrafted design and parameter tuning. Finally, by incorporating the numerical scheme as a solver into the proximal optimization algorithm, we derive an ODE-inspired Tuning-free Proximal Network, dubbed OTPNet, which achieves efficient and robust fusion reconstruction. Extensive experiments on nine datasets across three different remote sensing image fusion tasks show that our OTPNet outperforms existing state-of-the-art approaches, which validates the effectiveness of our method.

Most existing 3D visual speech animation methods synthesize lip movements synchronized with speech, which however neglect head poses and therefore degrade the animation realism. The animation of head poses presents two primary challenges: (1) the intricate mapping between speech and head poses remains poorly understood and (2) the absence of 4D face datasets featuring realistic head poses. Inspired by prosody decomposition in speech processing, we discern that head movements correlate with the fundamental frequency (F0) of speech prosody, while lip movements align with the language content. These observations motivate us to propose a novel framework, dubbed ProsodyTalker, that concurrently synthesizes lip and head movements, grounded in the principles of prosody decomposition. The core idea is first to adopt information perturbation to explicitly decompose the speech prosody into pose-related F0 and lip-related language content. Then, an autoregressive content-oriented fusion decoder is employed to enhance lip synchronization in the synthesized facial sequences. To synthesize head poses, we design a transformer-based variational autoencoder to learn a latent distribution of facial sequences and propose an F0-conditioned latent diffusion model to establish a probabilistic mapping from F0 to pose-related latent codes. Furthermore, we contribute a large-scale 4D face dataset containing bunches of variations in identities, head poses and facial motions. Extensive experiments show that our method achieves more realistic animation than state-of-the-art methods.

In this paper, we explore a novel image matting task aimed at achieving efficient inference under various computational cost constraints, specifically FLOP limitations, using a single matting network. Existing matting methods which have not explored scalable architectures or path-learning strategies, fail to tackle this challenge. To overcome these limitations, we introduce Path-Adaptive Matting (PAM), a framework that dynamically adjusts network paths based on image contexts and computational cost constraints. We formulate the training of the computational cost-constrained matting network as a bilevel optimization problem, jointly optimizing the matting network and the path estimator. Building on this formalization, we design a path-adaptive matting architecture by incorporating path selection layers and learnable connect layers to estimate optimal paths and perform efficient inference within a unified network. Furthermore, we propose a performance-aware path-learning strategy to generate path labels online by evaluating a few paths sampled from the prior distribution of optimal paths and network estimations, enabling robust and efficient online path learning. Experiments on five image matting datasets demonstrate that the proposed PAM framework achieves competitive performance across a range of computational cost constraints.

Traditional studies emphasize the significance of context information in improving matting performance. Consequently, deep learning-based matting methods delve into designing pooling or affinity-based context aggregation modules to achieve superior results. However, these modules cannot well handle the context scale shift caused by the difference in image size during training and inference, resulting in matting performance degradation. In this paper, we revisit the context aggregation mechanisms of matting networks and find that a basic encoder-decoder network without any context aggregation modules can actually learn more universal context aggregation, thereby achieving higher matting performance compared to existing methods. Building on this insight, we present AEMatter, a matting network that is straightforward yet very effective. AEMatter adopts a Hybrid-Transformer backbone with appearance-enhanced axis-wise learning (AEAL) blocks to build a basic network with strong context aggregation learning capability. Furthermore, AEMatter leverages a large image training strategy to assist the network in learning context aggregation from data. Extensive experiments on five popular matting datasets demonstrate that the proposed AEMatter outperforms state-of-the-art matting methods by a large margin. The source code is available at https://github.com/aipixel/AEMatter.

Existing super-resolution (SR) methods optimize all model weights equally using $\mathcal{L}_1$ or $\mathcal{L}_2$ losses by uniformly sampling image patches without considering dataset imbalances or parameter redundancy, which limits their performance. To address this, we formulate the image SR task as an imbalanced distribution transfer learning problem from a statistical probability perspective, proposing a plug-and-play Weight-Balancing framework (WBSR) to achieve balanced model learning without changing the original model structure and training data. Specifically, we develop a Hierarchical Equalization Sampling (HES) strategy to address data distribution imbalances, enabling better feature representation from texture-rich samples. To tackle model optimization imbalances, we propose a Balanced Diversity Loss (BDLoss) function, focusing on learning texture regions while disregarding redundant computations in smooth regions. After joint training of HES and BDLoss to rectify these imbalances, we present a gradient projection dynamic inference strategy to facilitate accurate and efficient inference. Extensive experiments across various models, datasets, and scale factors demonstrate that our method achieves comparable or superior performance to existing approaches with about 34\% reduction in computational cost.

Existing high-resolution image harmonization methods typically rely on global color adjustments or the upsampling of parameter maps. However, these methods ignore local variations, leading to inharmonious appearances. To address this problem, we propose an Adaptive-Interval Color Transformation method (AICT), which predicts pixel-wise color transformations and adaptively adjusts the sampling interval to model local non-linearities of the color transformation at high resolution. Specifically, a parameter network is first designed to generate multiple position-dependent 3-dimensional lookup tables (3D LUTs), which use the color and position of each pixel to perform pixel-wise color transformations. Then, to enhance local variations adaptively, we separate a color transform into a cascade of sub-transformations using two 3D LUTs to achieve the non-uniform sampling intervals of the color transform. Finally, a global consistent weight learning method is proposed to predict an image-level weight for each color transform, utilizing global information to enhance the overall harmony. Extensive experiments demonstrate that our AICT achieves state-of-the-art performance with a lightweight architecture. The code is available at https://github.com/aipixel/AICT.

Existing makeup transfer methods typically transfer simple makeup colors in a well-conditioned face image and fail to handle makeup style details (e.g., complicated colors and shapes) and facial occlusion. To address these problems, this paper proposes Hybrid Transformers with Attention-guided Spatial Embeddings (named HT-ASE) for makeup transfer and removal. Specifically, a makeup context extractor adopts makeup context global-local interactions to aggregate the high-level context and low-level detail features of the makeup styles, which obtains the context-aware makeup features that encode the complicated colors and shapes of the makeup styles. A face identity extractor adopts a face identity local interaction to aggregate the identity-relevant features of shallow layers into identity semantic features, which refines the identity features. A spatially similarity-aware fusion network introduces a spatially-adaptive layer-instance normalization with attention-guided spatial embeddings to perform semantic alignment and fusion between the makeup and identity features, yielding precise and robust transfer results even with large spatial misalignment and facial occlusion. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods, especially in the preservation of makeup style details and handling facial occlusion.

Most existing text-driven face image generation and manipulation methods are based on StyleGAN2, which is inherently limited to aligned faces and therefore makes these methods fail to preserve the highly variable face placement. Additionally, these methods directly leverage a pairwise loss to learn the correspondence between the image and text, which can not handle complex text descriptions, e.g., the text with multiple captions describes multiple facial attributes. To address these issues, we explore the feasibility of applying the more advanced StyleGAN3 to generate and manipulate the face images in an Open-World setup, e.g., the target face image is not required to be aligned and the text description contains multiple captions. To this end, we first design an improved iterative refinement strategy that adaptively predicts the generator weight offsets rather than residuals for the inverted latent code via a hypernetwork, which efficiently finds a desired generator with no image-specific optimization. We further analyze the disentanglement of different StyleGAN3 latent spaces and demonstrate that the${\mathcal {S}}$space learns a more semantically-disentangled representation. To enable complex edits mentioned by the multi-caption text, we propose a cross-modal feature filtration module with a probability adaptation strategy to capture the image-text correspondences. Finally, we incorporate a channel-wise attention mechanism to obtain a global latent manipulation direction, which learns to assign importance weights to different channels. Extensive experiments demonstrate the superior performance of our proposed method compared against the state-of-the-art methods.

Human instance matting aims to estimate an alpha matte for each human instance in an image, which is extremely challenging and has rarely been studied so far. Despite some efforts to use instance segmentation to generate a trimap for each instance and apply trimap-based matting methods, the resulting alpha mattes are often inaccurate due to inaccurate segmentation. In addition, this approach is computationally inefficient due to multiple executions of the matting method. To address these problems, this paper proposes a novel End-to-End Human Instance Matting (E2E-HIM) framework for simultaneous multiple instance matting in a more efficient manner. Specifically, a general perception network first extracts image features and decodes instance contexts into latent codes. Then, a united guidance network exploits spatial attention and semantics embedding to generate united semantics guidance, which encodes the locations and semantic correspondences of all instances. Finally, an instance matting network decodes the image features and united semantics guidance to predict all instance-level alpha mattes. In addition, we construct a large-scale human instance matting dataset (HIM-100K) comprising over 100,000 human images with instance alpha matte labels. Experiments on HIM-100K demonstrate the proposed E2E-HIM outperforms the existing methods on human instance matting with 50% lower errors and 5× faster speed (6 instances in a 640 × 640 image). Experiments on the PPM-100, RWP-636, and P3M datasets demonstrate that E2E-HIM also achieves competitive performance on traditional human matting.

Existing human matting methods are incapable of accurately estimating the alpha mattes of arbitrarily selected humans from a group photo. An alternative solution is to apply them to the corresponding cropped image patches. However, this option obtains an inaccurate alpha estimation due to the interference of the body parts of the neighboring humans. In addition, these methods are only trained on finely annotated synthetic data, which causes poor performance in real-world scenarios due to the domain shift. To address these problems, we propose human selective matting (HSMatt), which performs matting for arbitrarily selected humans from a group photo given only a simple bounding box as guidance. Specifically, we design a global–local context network to extract both local and global semantic context features. A human-aware trimap network is then proposed to generate human-aware trimaps for the selected humans, which adopts stacked bidirectional inference modules with intermediate supervision to progressively refine the estimated trimap. Finally, a partially supervised matting network is introduced to estimate the alpha matte, which uses a sample-varying loss to train the network on both the finely annotated synthetic data and coarsely annotated real-world data, resulting in high accuracy and good generalization. To evaluate the proposed HSMatt, we construct the first human selective matting dataset, named HSM-200K, which contains over 200,000 human images with instance-level alpha matte annotations. Experimental results demonstrate that the proposed HSMatt outperforms state-of-the-art methods.

The multi-channel blind rendezvous problem in distributed cognitive radio networks (DCRNs) refers to how users can hop to the same channel at the same time slot without any prior knowledge (i.e., each user is unaware of other users' information). The channel hopping (CH) technique is a typical solution to this blind rendezvous problem. In this paper, we propose a quinary coding and matrix structure-based CH algorithm called QCMS-CH. It can guarantee the rendezvous of users using only one cognitive radio in the scenario of the asynchronous clock (i.e., arbitrary time drift between users), heterogeneous channels (i.e., the available channel sets of users are distinct), and symmetric role (i.e., all users play a same role). The QCMS-CH algorithm first represents a randomly selected channel (denoted by R) as a fixed-length quaternary number. Then it encodes the quaternary number into a quinary bootstrapping sequence according to a carefully designed quaternary-quinary coding table with the prefix “R00”. Finally, it builds a CH matrix column by column according to the bootstrapping sequence and six different types of elaborately generated subsequences. The user can access the CH matrix row by row and accordingly perform its channel hopping to attempt to rendezvous with other users. We derive an upper bound on its Maximum Time-To-Rendezvous (MTTR). Simulation results show that QCMS-CH algorithm outperforms the state-of-the-art in terms of the MTTR and the Expected Time-To-Rendezvous (ETTR).

Image stitching aims to combine two images with overlapping fields to expand the field-of-view (FoV). However, the stitched images of existing methods are irregular, and need to be processed by rectangling methods, which is time-consuming and prone to be unnatural. In this paper, we propose the first end-to-end framework, Rectangular-output Deep Image Stitching Network (RDISNet), to directly stitch two images into a standard rectangular image while learning color consistency between image pairs and maintaining the authenticity of the content. To further preserve the structure of large objects in the stitched image, we design a dilated BN-RCU block to expand the receptive field of RDISNet for extracting enriched spatial context. Furthermore, we design a novel data synthesis pipeline and build the first rectangular-output deep image stitching dataset (RDIS-D) for jointing image stitching and rectangling. Experimental results demonstrate that RDISNet performs favorably against the state-of-the-art methods.

Object placement aims to insert a foreground object into a background image with a suitable location and size to create a natural composition. To predict a diverse distribution of placements, existing methods usually establish a one-to-one mapping from random vectors to the placements. However, these random vectors are not interpretable, which prevents users from interacting with the object placement process. To address this problem, we propose an Interactive Object Placement method with Reinforcement Learning, dubbed IOPRE, to make sequential decisions for producing a reasonable placement given an initial location and size of the foreground. We first design a novel action space to flexibly and stably adjust the location and size of the foreground while preserving its aspect ratio. Then, we propose a multi-factor state representation learning method, which integrates composition image features and sinusoidal positional embeddings of the foreground to make decisions for selecting actions. Finally, we design a hybrid reward function that combines placement assessment and the number of steps to ensure that the agent learns to place objects in the most visually pleasing and semantically appropriate location. Experimental results on the OPA dataset demonstrate that the proposed method achieves state-of-the-art performance in terms of plausibility and diversity.

Existing image-based virtual try-on methods directly transfer specific clothing to a human image without utilizing clothing attributes to refine the transferred clothing geometry and textures, which causes incomplete and blurred clothing appearances. In addition, these methods usually mask the limb textures of the input for the clothing-agnostic person representation, which results in inaccurate predictions for human limb regions (i.e., the exposed arm skin), especially when transforming between long-sleeved and short-sleeved garments. To address these problems, we present a progressive virtual try-on framework, named PL-VTON, which performs pixel-level clothing warping based on multiple attributes of clothing and embeds explicit limb-aware features to generate photo-realistic try-on results. Specifically, we design a Multi-attribute Clothing Warping (MCW) module that adopts a two-stage alignment strategy based on multiple attributes to progressively estimate pixel-level clothing displacements. A Human Parsing Estimator (HPE) is then introduced to semantically divide the person into various regions, which provides structural constraints on the human body and therefore alleviates texture bleeding between clothing and limb regions. Finally, we propose a Limb-aware Texture Fusion (LTF) module to estimate high-quality details in limb regions by fusing textures of the clothing and the human body with the guidance of explicit limb-aware features. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively.

Natural image matting estimates the alpha values of unknown regions in the trimap. Recently, deep learning based methods propagate the alpha values from the known regions to unknown regions according to the similarity between them. However, we find that more than 50% pixels in the unknown regions cannot be correlated to pixels in known regions due to the limitation of small effective reception fields of common convolutional neural networks, which leads to inaccurate estimation when the pixels in the unknown regions cannot be inferred only with pixels in the reception fields. To solve this problem, we propose Long-Range Feature Propagating Network (LFPNet), which learns the long-range context features outside the reception fields for alpha matte estimation. Specifically, we first design the propagating module which extracts the context features from the downsampled image. Then, we present Center-Surround Pyramid Pooling (CSPP) that explicitly propagates the context features from the surrounding context image patch to the inner center image patch. Finally, we use the matting module which takes the image, trimap and context features to estimate the alpha matte. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods on the AlphaMatting and Adobe Image Matting datasets.