In the challenging realm of image-to-image translation, most traditional methods require separate models for different translation directions, leading to inefficient use of computational resources. This paper introduces the Bidirectional Brownian Bridge Diffusion Model (BiBBDM), a novel approach that leverages Brownian Bridge processes for bidirectional image-to-image translation. Unlike conventional Diffusion Models (DMs) that treat image-to-image translation as a unidirectional conditional generation process, BiBBDM models the translation as a stochastic Brownian Bridge process, enabling simultaneous learning of bidirectional translation between two domains. This innovation allows our method to achieve bidirectional image translation using different sampling directions of a single model, eliminating the need for multiple models for both translation directions. To the best of our knowledge, BiBBDM is the first image translation framework to achieve simultaneous dual-domain sampling with the same model and parameters, based on Brownian Bridge diffusion processes. Extensive experimental results on various benchmarks demonstrate that BiBBDM achieves competitive performance, as evidenced by both visual inspection and quantitative metrics.

Most of the existing 3D talking face synthesis methods suffer from the lack of detailed facial expressions and realistic head poses, resulting in unsatisfactory experiences for users. In this article, we propose a novel pose-aware 3D talking face synthesis method with a novel geometry-guided audio-vertices attention. To capture more detailed expression, such as the subtle nuances of mouth shape and eye movement, we propose to build hierarchical audio features including a global attribute feature and a series of vertex-wise local latent movement features. Then, in order to fully exploit the topology of facial models, we further propose a novel geometry-guided audio-vertices attention module to predict the displacement of each vertex by using vertex connectivity relations to take full advantage of the corresponding hierarchical audio features. Finally, to accomplish pose-aware animation, we expand the existing database with an additional pose attribute, and a novel pose estimation module is proposed by paying attention to the whole head model. Numerical experiments demonstrate the effectiveness of the proposed method on realistic expression and head movements against state-of-the-art methods.

Text-driven image editing aims to manipulate images with the guidance of natural language description. Text is much more natural and intuitive than many other interaction modes, and attracts more attention recently. However, compared with classical supervised learning tasks, there is no standard benchmark dataset for text-driven interactive image editing up to now. Therefore, it is hard to train an end-to-end model for pixel-aligned interactive image editing driven by text. Some methods follow the paradigm of text-to-image models by incorporating the target image into the process of text-to-image generation. However, these methods relying on cross-modal text-to-image generation involve complicated and expensive models, which can lead to inconsistent editing effects. In this article, a novel text-driven image editing method is proposed. Our key observation is that this task can be more efficiently learned using image-to-image translation. To ensure effective learning for image editing, our framework takes paired text and the corresponding images for training, and disentangles each image into content and attributes, such that the content is maintained while the attributes are modified according to the text. Our network is a lightweight encoder-decoder architecture that accomplishes pixel-aligned end-to-end training via cycle-consistent supervision. Quantitative and qualitative experimental results show that the proposed method achieves state-of-the-art performance.

Image-to-image translation is an important and challenging problem in computer vision and image processing. Diffusion models (DM) have shown great potentials for high-quality image synthesis, and have gained competitive performance on the task of image-to-image translation. However, most of the existing diffusion models treat image-to-image translation as conditional generation processes, and suffer heavily from the gap between distinct domains. In this paper, a novel image-to-image translation method based on the Brownian Bridge Diffusion Model (BBDM) is proposed, which models image-to-image translation as a stochastic Brownian Bridge process, and learns the translation between two domains directly through the bidirectional diffusion process rather than a conditional generation process. To the best of our knowledge, it is the first work that proposes Brownian Bridge diffusion process for image-to-image translation. Experimental results on various benchmarks demonstrate that the proposed BBDM model achieves competitive performance through both visual inspection and measurable metrics.

The design of the light-weight infill structure is a hot research topic in additive manufacturing. In recent years, various infill structures have been proposed to reduce the amount of printing material. However, 3D models filled with them may have very different structural performances under different loading conditions. In addition, most of them are not self-supporting. To mitigate these issues, a novel light-weight infill structure based on the layer construction is proposed in this article. The layers of the proposed infill structure continuously and periodically transform between triangles and hexagons. The geometries of two adjacent layers are controlled to be self-supporting for different 3D printing technologies. The machine code (Gcode) of the filled 3D model is generated in the construction of the infill structure for 3D printers. That means 3D models filled with the proposed infill structure do not need an extra slicing process before printing, which is time consuming in some cases. Structural simulations and physical experiments demonstrate that our infill structure has comparable structural performance under different loading conditions. Furthermore, the relationship between the structural stiffness and the parameters of the infill structure is investigated, which will be helpful for non-professional users.

Mesh color edit propagation aims to propagate the color from a few color strokes to the whole mesh, which is useful for mesh colorization, color enhancement and color editing, etc. Compared with image edit propagation, luminance information is not available for 3D mesh data, so the color edit propagation is more difficult on 3D meshes than images, with far less research carried out. This paper proposes a novel solution based on sparse graph regularization. Firstly, a few color strokes are interactively drawn by the user, and then the color will be propagated to the whole mesh by minimizing a sparse graph regularized nonlinear energy function. The proposed method effectively measures geometric similarity over shapes by using a set of complementary multiscale feature descriptors, and effectively controls color bleeding via a sparse ℓ1 optimization rather than quadratic minimization used in existing work. The proposed framework can be applied for the task of interactive mesh colorization, mesh color enhancement and mesh color editing. Extensive qualitative and quantitative experiments show that the proposed method outperforms the state-of-the-art methods.

Given a reference colour image and a destination grayscale image, this paper presents a novel automatic colourisation algorithm that transfers colour information from the reference image to the destination image. Since the reference and destination images may contain content at different or even varying scales (due to changes of distance between objects and the camera), existing texture matching based methods can often perform poorly. We propose a novel cross-scale texture matching method to improve the robustness and quality of the colourisation results. Suitable matching scales are considered locally, which are then fused using global optimisation that minimises both the matching errors and spatial change of scales. The minimisation is efficiently solved using a multi-label graph-cut algorithm. Since only low-level texture features are used, texture matching based colourisation can still produce semantically incorrect results, such as meadow appearing above the sky. We consider a class of semantic violation where the statistics of up-down relationships learnt from the reference image are violated and propose an effective method to identify and correct unreasonable colourisation. Finally, a novel nonlocal ℓ1 optimisation framework is developed to propagate high confidence micro-scribbles to regions of lower confidence to produce a fully colourised image. Qualitative and quantitative evaluations show that our method outperforms several state-of-the-art methods.

The normals of feature points, i.e., the intersection points of multiple smooth surfaces, are ambiguous and undefined. This paper presents a unified definition for point cloud normals of feature and non-feature points, which allows feature points to possess multiple normals. This definition facilitates several succeeding operations, such as feature points extraction and point cloud filtering. We also develop a feature preserving normal estimation method which outputs multiple normals per feature point. The core of the method is a pair consistency voting scheme. All neighbor point pairs vote for the local tangent plane. Each vote takes the fitting residuals of the pair of points and their preliminary normal consistency into consideration. Thus the pairs from the same subspace and relatively far off features dominate the voting. An adaptive strategy is designed to overcome sampling anisotropy. In addition, we introduce an error measure compatible with traditional normal estimators, and present the first benchmark for normal estimation, composed of 152 synthesized data with various features and sampling densities, and 288 real scans with different noise levels. Comprehensive and quantitative experiments show that our method generates faithful feature preserving normals and outperforms previous cutting edge normal estimation methods, including the latest deep learning based method.

Benefiting from global rank constraints, the low-rank representation (LRR) method has been shown to be an effective solution to subspace learning. However, the global mechanism also means that the LRR model is not suitable for handling large-scale data or dynamic data. For large-scale data, the LRR method suffers from high time complexity, and for dynamic data, it has to recompute a complex rank minimization for the entire data set whenever new samples are dynamically added, making it prohibitively expensive. Existing attempts to online LRR either take a stochastic approach or build the representation purely based on a small sample set and treat new input as out-of-sample data. The former often requires multiple runs for good performance and thus takes longer time to run, and the latter formulates online LRR as an out-of-sample classification problem and is less robust to noise. In this paper, a novel online LRR subspace learning method is proposed for both large-scale and dynamic data. The proposed algorithm is composed of two stages: static learning and dynamic updating. In the first stage, the subspace structure is learned from a small number of data samples. In the second stage, the intrinsic principal components of the entire data set are computed incrementally by utilizing the learned subspace structure, and the LRR matrix can also be incrementally solved by an efficient online singular value decomposition algorithm. The time complexity is reduced dramatically for large-scale data, and repeated computation is avoided for dynamic problems. We further perform theoretical analysis comparing the proposed online algorithm with the batch LRR method. Finally, experimental results on typical tasks of subspace recovery and subspace clustering show that the proposed algorithm performs comparably or better than batch methods, including the batch LRR, and significantly outperforms state-of-the-art online methods.

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

Example-based color transfer is a critical operation in image editing but easily suffers from some corruptive artifacts in the mapping process. In this paper, we propose a novel unified color transfer framework with corruptive artifacts suppression, which performs iterative probabilistic color mapping with self-learning filtering scheme and multiscale detail manipulation scheme in minimizing the normalized Kullback-Leibler distance. First, an iterative probabilistic color mapping is applied to construct the mapping relationship between the reference and target images. Then, a self-learning filtering scheme is applied into the transfer process to prevent from artifacts and extract details. The transferred output and the extracted multi-levels details are integrated by the measurement minimization to yield the final result. Our framework achieves a sound grain suppression, color fidelity and detail appearance seamlessly. For demonstration, a series of objective and subjective measurements are used to evaluate the quality in color transfer. Finally, a few extended applications are implemented to show the applicability of this framework.

Geometry images are important representations of 3D geometry models. Smooth geometry images contributes to low approximation error and high image compressibility. We present an adapted parameterization method to generate a smooth geometry image. It is quite challenging to directly modify the parameter domain to smooth geometry images. Our novel idea is that we use an indirect way to construct a resulting parameter domain according to the desired geometry image. We first move image pixels according to the current parameter domain to decrease the local linear error. Then we formulate a relationship between the moved image pixels and the current parameter domain. Finally, we use the relationship to update the parameter domain by restituting the image pixels to their original positions. The process will continue until the local linear error is less than a given threshold or the number of iterations is larger than a given threshold. Experimental results illustrate that geometry images generated by our method have low linear errors and low approximation errors under different sampling resolutions.