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Differential Diffusion: Giving Each Pixel Its Strength

COMPUTER GRAPHICS FORUM 2025年

作者： Levin, Eran Fried, Ohad Tel Aviv Univ Tel Aviv Israel Reichman Univ Herzliyya Israel

Diffusion models have revolutionized image generation and editing, producing state-of-the-art results in conditioned and unconditioned image synthesis. While current techniques enable user control over the degree of change in an image edit, the controllability is limited to global changes over an entire edited region. This paper introduces a novel framework that enables customization of the amount of change per pixel or per image region. Our framework can be integrated into any existing diffusion model, enhancing it with this capability. Such granular control opens up a diverse array of new editing capabilities, such as control of the extent to which individual objects are modified, or the ability to introduce gradual spatial changes. Furthermore, we showcase the framework's effectiveness in soft-inpainting-the completion of portions of an image while subtly adjusting the surrounding areas to ensure seamless integration. Additionally, we introduce a new tool for exploring the effects of different change quantities. Our framework operates solely during inference, requiring no model training or fine-tuning. We demonstrate our method with the current open state-of-the-art models, and validate it via both quantitative and qualitative comparisons, and a user study. Our code is published and integrated into several platforms.

关键词： CCS Concepts • computing methodologies → image manipulation

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Succinct Palette and Color Model Generation and manipulation Using Hierarchical Representation

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COMPUTER GRAPHICS FORUM 2019年第7期38卷 1-10页

作者： Jeong, Taehong Yang, Myunghyun Shin, Hyun Joon Ajou Univ Life Media Interdisciplinary Programs Suwon South Korea

We propose a new method to obtain the representative colors and their distributions of an image. Our intuition is that it is possible to derive the global model from the local distributions. Beginning by sampling pure colors, we build a hierarchical representation of colors in the image via a bottom-up approach. From the resulting hierarchy, we can obtain satisfactory palettes/color models automatically without a predefined size. Furthermore, we provide interactive operations to manipulate the results which allow the users to reflect their intention directly. In our experiment, we show that the proposed method produces more succinct results that faithfully represent all the colors in the image with an appropriate number of components. We also show that the proposed interactive approach can improve the results of applications such as recoloring and soft segmentation.

关键词： CCS Concepts • computing methodologies → image manipulation Hierarchical representations

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Learning to Trace: Expressive Line Drawing Generation from Photographs

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COMPUTER GRAPHICS FORUM 2019年第7期38卷 69-80页

作者： Inoue, N. Ito, D. Xu, N. Yang, J. Price, B. Yamasaki, T. Univ Tokyo Tokyo Japan Adobe Res San Jose CA USA

In this paper, we present a new computational method for automatically tracing high-resolution photographs to create expressive line drawings. We define expressive lines as those that convey important edges, shape contours, and large-scale texture lines that are necessary to accurately depict the overall structure of objects (similar to those found in technical drawings) while still being sparse and artistically pleasing. Given a photograph, our algorithm extracts expressive edges and creates a clean line drawing using a convolutional neural network (CNN). We employ an end-to-end trainable fully-convolutional CNN to learn the model in a data-driven manner. The model consists of two networks to cope with two sub-tasks;extracting coarse lines and refining them to be more clean and expressive. To build a model that is optimal for each domain, we construct two new datasets for face/body and manga background. The experimental results qualitatively and quantitatively demonstrate the effectiveness of our model. We further illustrate two practical applications.

关键词： CCS Concepts • computing methodologies → image manipulation • Applied computing → Fine arts

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Infomages: Embedding Data into Thematic images

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COMPUTER GRAPHICS FORUM 2020年第3期39卷 593-606页

作者： Coelho, D. Mueller, K. SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

Recent studies have indicated that visually embellished charts such as infographics have the ability to engage viewers and positively affect memorability. Fueled by these findings, researchers have proposed a variety of infographic design tools. However, these tools do not cover the entire design space. In this work, we identify a subset of infographics that we call infomages. Infomages are casual visuals of data in which a data chart is embedded into a thematic image such that the content of the image reflects the subject and the designer's interpretation of the data. Creating an effective infomage, however, can require a fair amount of design expertise and is thus out of reach for most people. In order to also afford non-artists with the means to design convincing infomages, we first study the principled design of existing infomages and identify a set of key chart embedding techniques. Informed by these findings we build a design tool that links web-scale image search with a set of interactive image processing tools to empower novice users with the ability to design a wide variety of infomages. As the embedding process might introduce some amount of visual distortion of the data our tool also aids users to gauge the amount of this distortion, if any. We experimentally demonstrate the usability of our tool and conclude with a discussion of infomages and our design tool.

关键词： CCS Concepts • Human‐centered computing → Information visualization Visualization systems and tools • Applied computing → Media arts • computing methodologies → image manipulation

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High-Resolution Neural Face Swapping for Visual Effects

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COMPUTER GRAPHICS FORUM 2020年第4期39卷 173-184页

作者： Naruniec, J. Helminger, L. Schroers, C. Weber, R. M. Disney Res Studios Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland

In this paper, we propose an algorithm for fully automatic neural face swapping in images and videos. To the best of our knowledge, this is the first method capable of rendering photo-realistic and temporally coherent results at megapixel resolution. To this end, we introduce a progressively trained multi-way comb network and a light- and contrast-preserving blending method. We also show that while progressive training enables generation of high-resolution images, extending the architecture and training data beyond two people allows us to achieve higher fidelity in generated expressions. When compositing the generated expression onto the target face, we show how to adapt the blending strategy to preserve contrast and low-frequency lighting. Finally, we incorporate a refinement strategy into the face landmark stabilization algorithm to achieve temporal stability, which is crucial for working with high-resolution videos. We conduct an extensive ablation study to show the influence of our design choices on the quality of the swap and compare our work with popular state-of-the-art methods.

关键词： CCS Concepts • computing methodologies → image manipulation Unsupervised learning Neural networks

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Relighting Humans in the Wild: Monocular Full-Body Human Relighting with Domain Adaptation

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COMPUTER GRAPHICS FORUM 2021年第7期40卷 205-216页

作者： Tajima, D. Kanamori, Y. Endo, Y. Univ Tsukuba Tsukuba Ibaraki Japan

The modern supervised approaches for human image relighting rely on training data generated from 3D human models. However, such datasets are often small (e.g., Light Stage data with a small number of individuals) or limited to diffuse materials (e.g., commercial 3D scanned human models). Thus, the human relighting techniques suffer from the poor generalization capability and synthetic-to-real domain gap. In this paper, we propose a two-stage method for single-image human relighting with domain adaptation. In the first stage, we train a neural network for diffuse-only relighting. In the second stage, we train another network for enhancing non-diffuse reflection by learning residuals between real photos and images reconstructed by the diffuse-only network. Thanks to the second stage, we can achieve higher generalization capability against various cloth textures, while reducing the domain gap. Furthermore, to handle input videos, we integrate illumination-aware deep video prior to greatly reduce flickering artifacts even with challenging settings under dynamic illuminations.

关键词： CCS Concepts • computing methodologies → image manipulation Neural networks

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Line Art Colorization Based on Explicit Region Segmentation

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COMPUTER GRAPHICS FORUM 2021年第7期40卷 1-10页

作者： Cao, Ruizhi Mo, Haoran Gao, Chengying Sun Yat Sen Univ Guangzhou Peoples R China

Automatic line art colorization plays an important role in anime and comic industry. While existing methods for line art colorization are able to generate plausible colorized results, they tend to suffer from the color bleeding issue. We introduce an explicit segmentation fusion mechanism to aid colorization frameworks in avoiding color bleeding artifacts. This mechanism is able to provide region segmentation information for the colorization process explicitly so that the colorization model can learn to avoid assigning the same color across regions with different semantics or inconsistent colors inside an individual region. The proposed mechanism is designed in a plug-and-play manner, so it can be applied to a diversity of line art colorization frameworks with various kinds of user guidances. We evaluate this mechanism in tag-based and reference-based line art colorization tasks by incorporating it into the state-of-the-art models. Comparisons with these existing models corroborate the effectiveness of our method which largely alleviates the color bleeding artifacts.

关键词： CCS Concepts • computing methodologies → image manipulation Neural networks Computer vision

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CustomSketching: Sketch Concept Extraction for Sketch-based image Synthesis and Editing

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COMPUTER GRAPHICS FORUM 2024年第7期43卷

作者： Xiao, Chufeng Fu, Hongbo Hong Kong Univ Sci & Technol HKGAI Hong Kong Peoples R China City Univ Hong Kong Sch Creat Media Hong Kong Peoples R China Hong Kong Univ Sci & Technol Div Arts & Machine Creat Hong Kong Peoples R China

Personalization techniques for large text-to-image (T2I) models allow users to incorporate new concepts from reference images. However, existing methods primarily rely on textual descriptions, leading to limited control over customized images and failing to support fine-grained and local editing (e.g., shape, pose, and details). In this paper, we identify sketches as an intuitive and versatile representation that can facilitate such control, e.g., contour lines capturing shape information and flow lines representing texture. This motivates us to explore a novel task of sketch concept extraction: given one or more sketch-image pairs, we aim to extract a special sketch concept that bridges the correspondence between the images and sketches, thus enabling sketch-based image synthesis and editing at a fine-grained level. To accomplish this, we introduce CustomSketching, a two-stage framework for extracting novel sketch concepts via few-shot learning. Considering that an object can often be depicted by a contour for general shapes and additional strokes for internal details, we introduce a dual-sketch representation to reduce the inherent ambiguity in sketch depiction. We employ a shape loss and a regularization loss to balance fidelity and editability during optimization. Through extensive experiments, a user study, and several applications, we show our method is effective and superior to the adapted baselines.

关键词： CCS Concepts • computing methodologies → image manipulation

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Seamless and Aligned Texture Optimization for 3D Reconstruction

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COMPUTER GRAPHICS FORUM 2024年第7期43卷

作者： Wang, Lei Ge, Linlin Zhang, Qitong Feng, Jieqing Zhejiang Univ State Key Lab CAD&CG Zhejiang Peoples R China

Restoring the appearance of the model is a crucial step for achieving realistic 3D reconstruction. High-fidelity textures can also conceal some geometric defects. Since the estimated camera parameters and reconstructed geometry usually contain errors, subsequent texture mapping often suffers from undesirable visual artifacts such as blurring, ghosting, and visual seams. In particular, significant misalignment between the reconstructed model and the registered images will lead to texturing the mesh with inconsistent image regions. However, eliminating various artifacts to generate high-quality textures remains a challenge. In this paper, we address this issue by designing a texture optimization method to generate seamless and aligned textures for 3D reconstruction. The main idea is to detect misalignment regions between images and geometry and exclude them from texture mapping. To handle the texture holes caused by these excluded regions, a cross-patch texture hole-filling method is proposed, which can also synthesize plausible textures for invisible faces. Moreover, for better stitching of the textures from different views, an improved camera pose optimization is present by introducing color adjustment and boundary point sampling. Experimental results show that the proposed method can eliminate the artifacts caused by inaccurate input data robustly and produce high-quality texture results compared with state-of-the-art methods.

关键词： CCS Concepts • computing methodologies → image manipulation Texturing

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DiffPop: Plausibility-Guided Object Placement Diffusion for image Composition

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COMPUTER GRAPHICS FORUM 2024年第7期43卷

作者： Liu, Jiacheng Zhou, Hang Wei, Shida Ma, Rui Jilin Univ Changchun Peoples R China Simon Fraser Univ Burnaby BC Canada MOE Engn Res Ctr Knowledge Driven Human Machine Intell Changchun Peoples R China

In this paper, we address the problem of plausible object placement for the challenging task of realistic image composition. We propose DiffPop, the first framework that utilizes plausibility-guided denoising diffusion probabilistic model to learn the scale and spatial relations among multiple objects and the corresponding scene image. First, we train an unguided diffusion model to directly learn the object placement parameters in a self-supervised manner. Then, we develop a human-in-the-loop pipeline which exploits human labeling on the diffusion-generated composite images to provide the weak supervision for training a structural plausibility classifier. The classifier is further used to guide the diffusion sampling process towards generating the plausible object placement. Experimental results verify the superiority of our method for producing plausible and diverse composite images on the new Cityscapes-OP dataset and the public OPA dataset, as well as demonstrate its potential in applications such as data augmentation and multi-object placement tasks. Our dataset and code will be released.

关键词： CCS Concepts • computing methodologies → image manipulation Computer vision

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