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ASMR: Adaptive Skeleton-Mesh Rigging and Skinning via 2D Generative Prior

COMPUTER GRAPHICS FORUM 2025年

作者： Hong, Seokhyeon Choi, Soojin Kim, Chaelin Cha, Sihun Noh, Junyong Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea

Despite the growing accessibility of skeletal motion data, integrating it for animating character meshes remains challenging due to diverse configurations of both skeletons and meshes. Specifically, the body scale and bone lengths of the skeleton should be adjusted in accordance with the size and proportions of the mesh, ensuring that all joints are accurately positioned within the character mesh. Furthermore, defining skinning weights is complicated by variations in skeletal configurations, such as the number of joints and their hierarchy, as well as differences in mesh configurations, including their connectivity and shapes. While existing approaches have made efforts to automate this process, they hardly address the variations in both skeletal and mesh configurations. In this paper, we present a novel method for the automatic rigging and skinning of character meshes using skeletal motion data, accommodating arbitrary configurations of both meshes and skeletons. The proposed method predicts the optimal skeleton aligned with the size and proportion of the mesh as well as defines skinning weights for various meshskeleton configurations, without requiring explicit supervision tailored to each of them. By incorporating Diffusion 3D Features (Diff3F) as semantic descriptors of character meshes, our method achieves robust generalization across different configurations. To assess the performance ofour method in comparison to existing approaches, we conducted comprehensive evaluations encompassing both quantitative and qualitative analyses, specifically examining the predicted skeletons, skinning weights, and deformation quality.

关键词： CCS Concepts Mesh geometry models • computing methodologies → animation

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How to Train Your Dragon: Automatic Diffusion-Based Rigging for Characters with Diverse Topologies

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COMPUTER GRAPHICS FORUM 2025年

作者： Gu, Zeqi Liu, Difan Langlois, Timothy Fisher, Matthew Davis, Abe Cornell Tech New York NY 10044 USA Adobe Res San Francisco CA USA Cornell Univ Ithaca NY 14850 USA

Recent diffusion-based methods have achieved impressive results on animating images of human subjects. However, most of that success has built on human-specific body pose representations and extensive training with labeled real videos. In this work, we extend the ability of such models to animate images of characters with more diverse skeletal topologies. Given a small number (3-5) of example frames showing the character in different poses with corresponding skeletal information, our model quickly infers a rig for that character that can generate images corresponding to new skeleton poses. We propose a procedural data generation pipeline that efficiently samples training data with diverse topologies on the fly. We use it, along with a novel skeleton representation, to train our model on articulated shapes spanning a large space of textures and topologies. Then during fine-tuning, our model rapidly adapts to unseen target characters and generalizes well to rendering new poses, both for realistic and more stylized cartoon appearances. To better evaluate performance on this novel and challenging task, we create the first 2D video dataset that contains both humanoid and non-humanoid subjects with per-frame keypoint annotations. With extensive experiments, we demonstrate the superior quality of our results.

关键词： CCS Concepts Image manipulation • computing methodologies → animation

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Neural Face Skinning for Mesh-agnostic Facial Expression Cloning

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COMPUTER GRAPHICS FORUM 2025年

作者： Cha, Sihun Yoon, Serin Seo, Kwanggyoon Noh, Junyong Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea Flawless AI Santa Monica CA USA

Accurately retargeting facial expressions to a face mesh while enabling manipulation is a key challenge in facial animation retargeting. Recent deep-learning methods address this by encoding facial expressions into a global latent code, but they often fail to capture fine-grained details in local regions. While some methods improve local accuracy by transferring deformations locally, this often complicates overall control of the facial expression. To address this, we propose a method that combines the strengths of both global and local deformation models. Our approach enables intuitive control and detailed expression cloning across diverse face meshes, regardless of their underlying structures. The core idea is to localize the influence of the global latent code on the target mesh. Our model learns to predict skinning weights for each vertex of the target face mesh through indirect supervision from predefined segmentation labels. These predicted weights localize the global latent code, enabling precise and region-specific deformations even for meshes with unseen shapes. We supervise the latent code using Facial Action Coding System (FACS)-based blendshapes to ensure interpretability and allow straightforward editing of the generated animation. Through extensive experiments, we demonstrate improved performance over state-of-the-art methods in terms of expression fidelity, deformation transfer accuracy, and adaptability across diverse mesh structures.

关键词： CCS Concepts Machine learning Shape modeling • computing methodologies → animation

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Towards Robust Direction Invariance in Character animation

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COMPUTER GRAPHICS FORUM 2019年第7期38卷 235-242页

作者： Ma, Li-Ke Yang, Zeshi Guo, Baining Yin, KangKang Simon Fraser Univ Burnaby BC Canada Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

In character animation, direction invariance is a desirable property. That is, a pose facing north and the same pose facing south are considered the same;a character that can walk to the north is expected to be able to walk to the south in a similar style. To achieve such direction invariance, the current practice is to remove the facing direction's rotation around the vertical axis before further processing. Such a scheme, however, is not robust for rotational behaviors in the sagittal plane. In search of a smooth scheme to achieve direction invariance, we prove that in general a singularity free scheme does not exist. We further connect the problem with the hairy ball theorem, which is better-known to the graphics community. Due to the nonexistence of a singularity free scheme, a general solution does not exist and we propose a remedy by using a properly-chosen motion direction that can avoid singularities for specific motions at hand. We perform comparative studies using two deep-learning based methods, one builds kinematic motion representations and the other learns physics-based controls. The results show that with our robust direction invariant features, both methods can achieve better results in terms of learning speed and/or final quality. We hope this paper can not only boost performance for character animation methods, but also help related communities currently not fully aware of the direction invariance problem to achieve more robust results.

关键词： CCS Concepts • computing methodologies → animation • Mathematics of computing → Algebraic topology

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Deep Learning-Based Unsupervised Human Facial Retargeting

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COMPUTER GRAPHICS FORUM 2021年第7期40卷 45-55页

作者： Kim, Seonghyeon Jung, Sunjin Seo, Kwanggyoon Blanco i Ribera, Roger Noh, Junyong Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea C JeS Gulliver Studios Goyang South Korea

Traditional approaches to retarget existing facial blendshape animations to other characters rely heavily on manually paired data including corresponding anchors, expressions, or semantic parametrizations to preserve the characteristics of the original performance. In this paper, inspired by recent developments in face swapping and reenactment, we propose a novel unsupervised learning method that reformulates the retargeting of 3D facial blendshape-based animations in the image domain. The expressions of a source model is transferred to a target model via the rendered images of the source animation. For this purpose, a reenactment network is trained with the rendered images of various expressions created by the source and target models in a shared latent space. The use of shared latent space enable an automatic cross-mapping obviating the need for manual pairing. Next, a blendshape prediction network is used to extract the blendshape weights from the translated image to complete the retargeting of the animation onto a 3D target model. Our method allows for fully unsupervised retargeting of facial expressions between models of different configurations, and once trained, is suitable for automatic real-time applications.

关键词： CCS Concepts • computing methodologies → animation Computer vision

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Illustrative Motion Smoothing for Attention Guidance in Dynamic Visualizations

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COMPUTER GRAPHICS FORUM 2023年第3期42卷 361-372页

作者： Eschner, Johannes Mindek, Peter Waldner, Manuela TU Wien Vienna Austria Nanographics GmbH Vienna Austria

3D animations are an effective method to learn about complex dynamic phenomena, such as mesoscale biological processes. The animators' goals are to convey a sense of the scene's overall complexity while, at the same time, visually guiding the user through a story of subsequent events embedded in the chaotic environment. Animators use a variety of visual emphasis techniques to guide the observers' attention through the story, such as highlighting, halos -- or by manipulating motion parameters of the scene. In this paper, we investigate the effect of smoothing the motion of contextual scene elements to attract attention to focus elements of the story exhibiting high-frequency motion. We conducted a crowdsourced study with 108 participants observing short animations with two illustrative motion smoothing strategies: geometric smoothing through noise reduction of contextual motion trajectories and visual smoothing through motion blur of rev{context} items. We investigated the observers' ability to follow the story as well as the effect of the techniques on speed perception in a molecular scene. Our results show that moderate motion blur significantly improves users' ability to follow the story. Geometric motion smoothing is less effective but increases the visual appeal of the animation. However, both techniques also slow down the perceived speed of the animation. We discuss the implications of these results and derive design guidelines for animators of complex dynamic visualizations.

关键词： CCS Concepts • Human-centered computing → Empirical studies in visualization • computing methodologies → animation

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Fracture Patterns Design for Anisotropic Models with the Material Point Method

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COMPUTER GRAPHICS FORUM 2020年第7期39卷 93-104页

作者： Cao, Wei Lyu, Luan Ren, Xiaohua Zhang, Bob Yang, Zhixin Wu, Enhua Univ Macau IFST Taipa Macao Peoples R China Univ Macau SKL IOTSC Taipa Macao Peoples R China Chinese Acad Sci IOS SKLCS Beijing Peoples R China Univ CAS Beijing Peoples R China Tencent Youtu Beijing Peoples R China

Physically plausible fracture animation is a challenging topic in computer graphics. Most of the existing approaches focus on the fracture of isotropic materials. We proposed a frame-field method for the design of anisotropic brittle fracture patterns. In this case, the material anisotropy is determined by two parts: anisotropic elastic deformation and anisotropic damage mechanics. For the elastic deformation, we reformulate the constitutive model of hyperelastic materials to achieve anisotropy by adding additional energy density functions in particular directions. For the damage evolution, we propose an improved phase-field fracture method to simulate the anisotropy by designing a deformation-aware second-order structural tensor. These two parts can present elastic anisotropy and fractured anisotropy independently, or they can be well coupled together to exhibit rich crack effects. To ensure the flexibility of simulation, we further introduce a frame-field concept to assist in setting local anisotropy, similar to the fiber orientation of textiles. For the discretization of the deformable object, we adopt a novel Material Point Method(MPM) according to its fracture-friendly nature. We also give some design criteria for anisotropic models through comparative analysis. Experiments show that our anisotropic method is able to be well integrated with the MPM scheme for simulating the dynamic fracture behavior of anisotropic materials.

关键词： CCS Concepts • computing methodologies → animation Physical simulation

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