ACM TRANSACTIONS ON GRAPHICS (TOG) 是一份经过同行评审的科学期刊，旨在传播计算机图形学领域的最新发现。它自 1982 年以来由计算机协会出版。从 2003 年开始，所有在 SIGGRAPH 年度会议上被接受发表的论文都印在该期刊的夏季特刊上。

ACM Transactions on Graphics (TOG) is a peer-reviewed scientific journal aimed at disseminating the latest research findings in the field of computer graphics. It has been published by the Association for Computing Machinery since 1982. Since 2003, all papers accepted for presentation at the SIGGRAPH annual conference are included in the summer issue of this journal.

《ACM Transactions on Graphics》（TOG）是一本经过同行评审的科学期刊，旨在传播计算机图形学领域的最新研究成果。该期刊自1982年由计算机协会出版。从2003年起，所有在SIGGRAPH年度会议上接受发表的论文均会收录在该期刊的夏季特刊中。

ACM Transactions on Graphics

We propose a novel system for portrait relighting and background replacement, which maintains high-frequency boundary details and accurately synthesizes the subject's appearance as lit by novel illumination, thereby producing realistic composite images for any desired scene. Our technique includes foreground estimation via alpha matting, relighting, and compositing. We demonstrate that each of these stages can be tackled in a sequential pipeline without the use of priors (e.g. known background or known illumination) and with no specialized acquisition techniques, using only a single RGB portrait image and a novel, target HDR lighting environment as inputs. We train our model using relit portraits of subjects captured in a light stage computational illumination system, which records multiple lighting conditions, high quality geometry, and accurate alpha mattes. To perform realistic relighting for compositing, we introduce a novel per-pixel lighting representation in a deep learning framework, which explicitly models the diffuse and the specular components of appearance, producing relit portraits with convincingly rendered non-Lambertian effects like specular highlights. Multiple experiments and comparisons show the effectiveness of the proposed approach when applied to in-the-wild images.

Rohit Pandey

Sergio Orts Escolano

Chloe Legendre

Christian Häne

Sofien Bouaziz

Christoph Rhemann

Paul Debevec

Sean Fanello

ACM Trans. Graph.

Total relighting

Children’s drawings have a wonderful inventiveness, creativity, and variety to them. We present a system that automatically animates children’s drawings of the human figure, is robust to the variance inherent in these depictions, and is simple and straightforward enough for anyone to use. We demonstrate the value and broad appeal of our approach by building and releasing the Animated Drawings Demo, a freely available public website that has been used by millions of people around the world. We present a set of experiments exploring the amount of training data needed for fine-tuning, as well as a perceptual study demonstrating the appeal of a novel twisted perspective retargeting technique. Finally, we introduce the Amateur Drawings Dataset, a first-of-its-kind annotated dataset, collected via the public demo, containing over 178,000 amateur drawings and corresponding user-accepted character bounding boxes, segmentation masks, and joint location annotations.

Harrison Jesse Smith

Qingyuan Zheng

Yifei Li

Somya Jain

Jessica K. Hodgins

A Method for Animating Children’s Drawings of the Human Figure

一种用于动画化儿童人体图像绘画的方法

Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbedEdgeConvsuitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS.

Yue Wang

Yongbin Sun

Ziwei Liu

Sanjay E. Sarma

Michael M. Bronstein

Justin M. Solomon

Dynamic Graph CNN for Learning on Point Clouds

用于点云学习的动态图卷积神经网络

Neural rendering algorithms introduce a fundamentally new approach for photorealistic rendering, typically by learning a neural representation of illumination on large numbers of ground truth images. When training for a given variable scene, such as changing objects, materials, lights, and viewpoint, the space \( \mathcal {D} \) of possible training data instances quickly becomes unmanageable as the dimensions of variable parameters increase. We introduce a novel Active Exploration method using Markov Chain Monte Carlo, which explores \( \mathcal {D} \) , generating samples (i.e., ground truth renderings) that best help training and interleaves training and on-the-fly sample data generation. We introduce a self-tuning sample reuse strategy to minimize the expensive step of rendering training samples. We apply our approach on a neural generator that learns to render novel scene instances given an explicit parameterization of the scene configuration. Our results show that Active Exploration trains our network much more efficiently than uniformly sampling and, together with our resolution enhancement approach, achieves better quality than uniform sampling at convergence. Our method allows interactive rendering of hard light transport paths (e.g., complex caustics), which require very high samples counts to be captured, and provides dynamic scene navigation and manipulation, after training for 5 to 18 hours depending on required quality and variations.

Stavros Diolatzis

Julien Philip

George Drettakis

Active Exploration for Neural Global Illumination of Variable Scenes

可变场景神经全局光照的主动探索

Learning from multi-view images using neural implicit signed distance functions shows impressive performance on 3D Reconstruction of opaque objects. However, existing methods struggle to reconstruct accurate geometry when applied to translucent objects due to the non-negligible bias in their rendering function. To address the inaccuracies in the existing model, we have reparameterized the density function of the neural radiance field by incorporating an estimated constant extinction coefficient. This modification forms the basis of our innovative framework, which is geared towards highfidelity surface reconstruction and the novel-view synthesis of translucent objects. Our framework contains two stages. In the reconstruction stage, we introduce a novel weight function to achieve accurate surface geometry reconstruction. Following the recovery of geometry, the second phase involves learning the distinct scattering properties of the participating media to enhance rendering. A comprehensive dataset, comprising both synthetic and real translucent objects, has been built for conducting extensive experiments. Experiments reveal that our method outperforms existing approaches in terms of reconstruction and novel-view synthesis.

Yuxiang Cai

Jiaxiong Qiu

Zhong Li

Bo Ren

NeuralTO: Neural Reconstruction and View Synthesis of Translucent Objects

NeuralTO：半透明物体的神经重建与视图合成

Creating an appearance-preserving level-of-detail (LoD) representation for arbitrary 3D scenes is a challenging problem. The appearance of a scene is an intricate combination of both geometry and material models, and is further complicated by correlation due to the spatial configuration of scene elements. We present a novel volumetric representation for the aggregated appearance of complex scenes and a pipeline for LoD generation and rendering. The core of our representation is the Aggregated Bidirectional Scattering Distribution Function (ABSDF) that summarizes the far-field appearance of all surfaces inside a voxel. We propose a closed-form factorization of the ABSDF that accounts for spatially varying and orientation-varying material parameters. We tackle the challenge of capturing the correlation existing locally within a voxel and globally across different parts of the scene. Our method faithfully reproduces appearance and achieves higher quality than existing scene filtering methods. The memory footprint and rendering cost of our representation are decoupled from the original scene complexity.

Yang Zhou

Tao Huang

Ravi Ramamoorthi

Pradeep Sen

Ling-Qi Yan

Appearance-Preserving Scene Aggregation for Level-of-Detail Rendering

外观保持的场景聚合用于细节层次渲染

Fluid droplets behave significantly different from larger fluid bodies. At smaller scales, surface tension and friction between fluids and the boundary play an essential role and are even able to counteract gravitational forces. There are quite a few existing approaches that model surface tension forces within an SPH environment. However, as often as not, physical correctness and simulation stability are still major concerns with many surface tension formulations. We propose a new approach to compute surface tension that is both robust and produces the right amount of surface tension. Conversely, less attention was given to friction forces at the fluid-boundary interface. Recent experimental research indicates that Coulomb friction can be used to describe the behavior of droplets resting on a slope. Motivated by this, we develop a novel friction force formulation at the fluid-boundary interface following the Coulomb model, which allows us to replicate a new range of well known fluid behavior such as the motion of rain droplets on a window pane. Both forces are combined with an IISPH variant into one unified solver that is able to simultaneously compute strongly coupled surface tension, friction and pressure forces.

Timo Probst

Matthias Teschner

Unified Pressure, Surface Tension and Friction for SPH Fluids

SPH流体的统一压力、表面张力和摩擦

Image translation has various applications in computer graphics and computer vision, aiming to transfer images from one domain to another. Thanks to the excellent generation capability of diffusion models, recent single-view image translation methods achieve realistic results. However, directly applying diffusion models for multi-view image translation remains challenging for two major obstacles: the need for paired training data and the limited view consistency. To overcome the obstacles, we present a first unified multi-view image to multi-view image translation framework based on diffusion models, called MV2MV. Firstly, we propose a novel self-supervised training strategy that exploits the success of off-the-shelf single-view image translators and the 3D Gaussian Splatting (3DGS) technique to generate pseudo ground truths as supervisory signals, leading to enhanced consistency and fine details. Additionally, we propose a latent multi-view consistency block, which utilizes the latent-3DGS as the underlying 3D representation to facilitate information exchange across multi-view images and inject 3D prior into the diffusion model to enforce consistency. Finally, our approach simultaneously optimizes the diffusion model and 3DGS to achieve a better trade-off between consistency and realism. Extensive experiments across various translation tasks demonstrate that MV2MV outperforms task-specific specialists in both quantitative and qualitative.

Youcheng Cai

Runshi Li

Ligang Liu

MV2MV: Multi-View Image Translation via View-Consistent Diffusion Models

MV2MV：通过视图一致扩散模型实现多视图图像转换

One of the primary reasons for the high cost of traditional animation is the inbetweening process, where artists manually draw each intermediate frame necessary for smooth motion. Making this process more efficient has been at the core of computer graphics research for years, yet the industry has adopted very few solutions. Most existing solutions either require vector input or resort to tight inbetweening; often, they attempt to fully automate the process. In industry, however, keyframes are often spaced far apart, drawn in raster format, and contain occlusions. Moreover, inbetweening is fundamentally an artistic process, so the artist should maintain high-level control over it. We address these issues by proposing a novel inbetweening system for bitmap character drawings, supporting both tight and far inbetweening. In our setup, the artist can control motion by animating a skeleton between the keyframe poses. Our system then performs skeleton-based deformation of the bitmap drawings into the same pose and employs discrete optimization and deep learning to blend the deformed images. Besides the skeleton and the two drawn bitmap keyframes, we require very little annotation. However, deforming drawings with occlusions is complex, as it requires a piecewise smooth deformation field. To address this, we observe that this deformation field is smooth when the drawing is lifted into 3D. Our system therefore optimizes topology of a 2.5D partially layered template that we use to lift the drawing into 3D and get the final piecewise-smooth deformaton, effectively resolving occlusions. We validate our system through a series of animations, qualitative and quantitative comparisons, and user studies, demonstrating that our approach consistently outperforms the state of the art and our results are consistent with the viewers' perception. Code and data for our paper are available at http://www-labs.iro.umontreal.ca/~bmpix/inbetweening/.

Kirill Brodt

Mikhail Bessmeltsev

Skeleton-Driven Inbetweening of Bitmap Character Drawings

位图字符绘图的骨架驱动中间帧生成

Reduced-order simulation is an emerging method for accelerating physical simulations with high DOFs, and recently developed neural-network-based methods with nonlinear subspaces have been proven effective in diverse applications as more concise subspaces can be detected. However, the complexity and landscape of simulation objectives within the subspace have not been optimized, which leaves room for enhancement of the convergence speed. This work focuses on this point by proposing a general method for finding optimized subspace mappings, enabling further acceleration of neural reduced-order simulations while capturing comprehensive representations of the configuration manifolds. We achieve this by optimizing the Lipschitz energy of the elasticity term in the simulation objective, and incorporating the cubature approximation into the training process to manage the high memory and time demands associated with optimizing the newly introduced energy. Our method is versatile and applicable to both supervised and unsupervised settings for optimizing the parameterizations of the configuration manifolds. We demonstrate the effectiveness of our approach through general cases in both quasi-static and dynamics simulations. Our method achieves acceleration factors of up to 6.83 while consistently preserving comparable simulation accuracy in various cases, including large twisting, bending, and rotational deformations with collision handling. This novel approach offers significant potential for accelerating physical simulations, and can be a good add-on to existing neural-network-based solutions in modeling complex deformable objects.

Aoran Lyu

Shixian Zhao

Chuhua Xian

Zhihao Cen

Hongmin Cai

Guoxin Fang

Accelerate Neural Subspace-Based Reduced-Order Solver of Deformable Simulation by Lipschitz Optimization

通过Lipschitz优化加速基于神经子空间的可变形模拟降阶求解器

Abbreviation	ACM Trans. Graphics
Journal Impact	7.84
Quartiles(Global)	COMPUTER SCIENCE, SOFTWARE ENGINEERING(Q1)

ISSN	0730-0301, 1557-7368
h-index	251
Top Journals	Yes

Publication Information	Publisher: Association for Computing Machinery (ACM)，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1982，Proportion of original research papers: 100.00%，Self Citation Rate:19.20%， Gold OA Rate: 4.06%
Average review cycle	网友分享经验：平均12.0个月
Average recruitment ratio	网友分享经验：很难

ACM Transactions on Graphics

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