Controllable character image animation has a wide range of applications. Although existing studies have consistently improved performance, challenges persist in the field of character image animation, particularly concerning stability in complex backgrounds and tasks involving multiple characters. To address these challenges, we propose a novel multi-condition guided framework for character image animation, employing several well-designed input modules to enhance the implicit decoupling capability of the model. First, the optical flow guider calculates the background optical flow map as guidance information, which enables the model to implicitly learn to decouple the background motion into background constants and background momentum during training, and generate a stable background by setting zero background momentum during inference. Second, the depth order guider calculates the order map of the characters, which transforms the depth information into the positional information of multiple characters. This facilitates the implicit learning of decoupling different characters, especially in accurately separating the occluded body parts of multiple characters. Third, the reference pose map is input to enhance the ability to decouple character texture and pose information in the reference image. Furthermore, to fill the gap of fair evaluation of multi-character image animation, we propose a new benchmark comprising about 4,000 frames. Extensive qualitative and quantitative evaluations demonstrate that our method excels in generating high-quality character animations, especially in scenarios of complex backgrounds and multiple characters.
We propose novel motion representations for animating articulated objects consisting of distinct parts. In a completely unsupervised manner, our method identifies object parts, tracks them in a driving video, and infers their motions by considering their principal axes. In contrast to the previous keypoint-based works, our method extracts meaningful and consistent regions, describing locations, shape, and pose. The regions correspond to semantically relevant and distinct object parts, that are more easily detected in frames of the driving video. To force decoupling of foreground from backgroun…
Quantization has been applied to multiple domains in Deep Neural Networks (DNNs). We propose Depthwise Quantization (DQ) where $\textit{quantization}$ is applied to a decomposed sub-tensor along the $\textit{feature axis}$ of weak statistical dependence. The feature decomposition leads to an exponential increase in $\textit{representation capacity}$ with a linear increase in memory and parameter cost. In addition, DQ can be directly applied to existing encoder-decoder frameworks without modification of the DNN architecture. We use DQ in the context of Hierarchical Auto-Encoder and train end-to…
We present a new implicit warping framework for image animation using sets of source images through the transfer of the motion of a driving video. A single cross- modal attention layer is used to find correspondences between the source images and the driving image, choose the most appropriate features from different source images, and warp the selected features. This is in contrast to the existing methods that use explicit flow-based warping, which is designed for animation using a single source and does not extend well to multiple sources. The pick-and-choose capability of our framework helps…
Animate-X++: Universal Character Image Animation with Dynamic Backgrounds
School of Computing and Data Science, The University of Hong Kong（计算与数据科学学院，香港大学）；Ant Group（蚂蚁集团）；College of Letters and Science, The University of Wisconsin-Madison（文理学院，威斯康星大学麦迪逊分校）；College of Computer Science, University of North Carolina at Chapel Hill（计算机科学学院，北卡罗来纳大学教堂山分校）；School of Software, Tsinghua University（软件学院，清华大学）
IM-Animation: An Implicit Motion Representation for Identity-decoupled Character Animation
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Kling Team, Kuaishou Technology（快手技术 Kling 团队）；Cardiff University（卡迪夫大学）
LINR Bridge: Vector Graphic Animation via Neural Implicits and Video Diffusion Priors
Wangxuan Institute of Computer Technology, State Key Laboratory of Multimedia Information Processing, \ University, Beijing, China；Wangxuan Institute of Computer Technology, State Key Laboratory of Multimedia Information Processing, Peking University, Beijing, China
ZMO AI Inc.（ZMO人工智能公司）；ShanghaiTech University（上海科技大学）；Nanyang Technological University（南洋理工大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程研究中心）；Shanghai Engineering Research Center of Energy Efficient and Custom AI IC（上海高效节能与定制人工智能芯片工程研究中心）
We propose united implicit functions (UNIF), a part-based method for clothed human reconstruction and animation with raw scans and skeletons as the input. Previous part-based methods for human reconstruction rely on ground-truth part labels from SMPL and thus are limited to minimal-clothed humans. In contrast, our method learns to separate parts from body motions instead of part supervision, thus can be extended to clothed humans and other articulated objects. Our Partition-from-Motion is achieved by a bone-centered initialization, a bone limit loss, and a section normal loss that ensure stabl…
LSF-Animation: Label-Free Speech-Driven Facial Animation via Implicit Feature Representation
University of Chinese Academy of Sciences（中国科学院大学）；Zhongguancun Academy（中关村学院）；National University of Singapore（新加坡国立大学）；Chongqing University（重庆大学）；University of Science and Technology of China（中国科学技术大学）
Mamba-Enhanced Implicit Motion Learning for Audio-Driven Portrait Animation
Fujian Provincial Natural Science Foundation of China（福建省自然科学基金委员会）；Giant Interactive Group Inc.（巨匠互动集团有限公司）；National Natural Science Foundation of China（国家自然科学基金委员会）
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven Facial Animation
Beihang University（北京航空航天大学）；Peng Cheng Laboratory（鹏城实验室）；North China University of Technology（华北理工大学）；University of Washington（华盛顿大学）；University of Copenhagen（哥本哈根大学）
Shenzhen Research Institute of Big Data（深圳大数据研究院）；Tencent America（腾讯美国）
Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm. However, reconstructing a 3D caricature from a 2D caricature image remains a challenging task, mostly due to the lack of data. We propose to fill this gap by introducing 3DCaricShop, the first large-scale 3D caricature dataset that contains 2000 high-quality diversified 3D caricatures manually crafted by professional artists. 3DCaricShop also provides rich annotations including a paired 2D caricature image, camera parameters and 3D facial landmarks. To dem…
NVIDIA（英伟达）；University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）；Vector Institute（矢量研究所）
We present HIPNet, a neural implicit pose network trained on multiple subjects across many poses. HIPNet can disentangle subject-specific details from pose-specific details, effectively enabling us to retarget motion from one subject to another or to animate between keyframes through latent space interpolation. To this end, we employ a hierarchical skeleton-based representation to learn a signed distance function on a canonical unposed space. This joint-based decomposition enables us to represent subtle details that are local to the space around the body joint. Unlike previous neural implicit …
Motion transfer is the task of synthesizing future video frames of a single source image according to the motion from a given driving video. In order to solve it, we face the challenging complexity of motion representation and the unknown relations between the driving video and the source image. Despite its difficulty, this problem attracted great interests from researches at the recent years, with gradual improvements. The goal is often thought as the decoupling of motion and appearance, which is may be solved by extracting the motion from keypoint movement. We chose to tackle the generic, un…
Nerf-based Generative models have shown impressive capacity in generating high-quality images with consistent 3D geometry. Despite successful synthesis of fake identity images randomly sampled from latent space, adopting these models for generating face images of real subjects is still a challenging task due to its so-called inversion issue. In this paper, we propose a universal method to surgically fine-tune these NeRF-GAN models in order to achieve high-fidelity animation of real subjects only by a single image. Given the optimized latent code for an out-of-domain real image, we employ 2D lo…
Zero-shot High-fidelity and Pose-controllable Character Animation
Fudan University（复旦大学）；Shanghai Key Lab of Intell. Info. Processing（上海智能信息处理重点实验室）；School of CS, Fudan University（复旦大学计算机科学技术学院）；Shanghai Collaborative Innovation Center of Intelligent Visual Computing（上海智能视觉计算协同创新中心）；OPPO AI Center（OPPO人工智能中心）；South China University Of Technology（华南理工大学）；Westlake University（西湖大学）
The recent increase in popularity of volumetric representations for scene reconstruction and novel view synthesis has put renewed focus on animating volumetric content at high visual quality and in real-time. While implicit deformation methods based on learned functions can produce impressive results, they are `black boxes' to artists and content creators, they require large amounts of training data to generalise meaningfully, and they do not produce realistic extrapolations outside the training data. In this work we solve these issues by introducing a volume deformation method which is real-t…
Decoupling Dark Knowledge via Block-wise Logit Distillation for Feature-level Alignment
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；ZJU-UIUC Institute, Zhejiang University（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合学院）
A unique challenge in creating high-quality animatable and relightable 3D avatars of people is modeling human eyes. The challenge of synthesizing eyes is multifold as it requires 1) appropriate representations for the various components of the eye and the periocular region for coherent viewpoint synthesis, capable of representing diffuse, refractive and highly reflective surfaces, 2) disentangling skin and eye appearance from environmental illumination such that it may be rendered under novel lighting conditions, and 3) capturing eyeball motion and the deformation of the surrounding skin to en…
University of Hamburg（汉堡大学）；Adobe Research（Adobe 研究院）
We introduce CharacterGAN, a generative model that can be trained on only a few samples (8 - 15) of a given character. Our model generates novel poses based on keypoint locations, which can be modified in real time while providing interactive feedback, allowing for intuitive reposing and animation. Since we only have very limited training samples, one of the key challenges lies in how to address (dis)occlusions, e.g. when a hand moves behind or in front of a body. To address this, we introduce a novel layering approach which explicitly splits the input keypoints into different layers which are…
LocalDyGS: Multi-view Global Dynamic Scene Modeling via Adaptive Local Implicit Feature Decoupling
Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology Shenzhen Graduate School, Peking University（广东省超高清沉浸媒体技术重点实验室 香港大学深圳研究生院）；Pengcheng Lab（鹏城实验室）；University of Birmingham（伯明翰大学）
Generating talking head videos through a face image and a piece of speech audio still contains many challenges. ie, unnatural head movement, distorted expression, and identity modification. We argue that these issues are mainly because of learning from the coupled 2D motion fields. On the other hand, explicitly using 3D information also suffers problems of stiff expression and incoherent video. We present SadTalker, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio and implicitly modulates a novel 3D-aware face render for talking head generation. To learn th…
Unlock Pose Diversity: Accurate and Efficient Implicit Keypoint-based Spatiotemporal Diffusion for Audio-driven Talking Portrait
University of Liverpool（利物浦大学）；Ant Group（蚂蚁集团）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Duke Kunshan University（昆山杜克大学）；Ricoh Software Research Center（理光软件研究中心）
Euler-inspired Decoupling Neural Operator for Efficient Pansharpening
School of Software Technology, Zhejiang University（浙江大学软件技术学院）；School of Computer Science and Technology , Chongqing University of Post and Telecommunications（重庆邮电大学计算机科学与技术学院）
School of Computer and Communication Sciences, EPFL（洛桑联邦理工学院计算机与通信科学学院）
Classic image-restoration algorithms use a variety of priors, either implicitly or explicitly. Their priors are hand-designed and their corresponding weights are heuristically assigned. Hence, deep learning methods often produce superior image restoration quality. Deep networks are, however, capable of inducing strong and hardly predictable hallucinations. Networks implicitly learn to be jointly faithful to the observed data while learning an image prior; and the separation of original data and hallucinated data downstream is then not possible. This limits their wide-spread adoption in image r…
StdGEN++: A Comprehensive System for Semantic-Decomposed 3D Character Generation
Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Tencent AIPD（腾讯AIPD）；School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）
Continuous Splatting meets Retinex: Continuous Gaussian Splatting and Implicit Reflectance Modeling for Low-Light Image Enhancement
College of Mechanical and Vehicle Engineering, Chongqing University（重庆大学机械与运载工程学院）；School of Engineering Science, University of Science and Technology of China（中国科学技术大学工程科学学院）；National Innovation Center of Intelligent and Connected Vehicles（国家智能网联汽车创新中心）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院）
