CVPR 2024 论文列表

This paper studies the human image animation task which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. Despite achieving reasonable results these approaches face challenges in maintaining temporal consistency throughout the animation due to the lack of temporal modeling and poor preservation of reference identity. In this work we introduce MagicAnimate a diffusion-based framework that aims at enhancing temporal consistency preserving reference image faithfully and improving animation fidelity. To achieve this we first develop a video diffusion model to encode temporal information. Second to maintain the appearance coherence across frames we introduce a novel appearance encoder to retain the intricate details of the reference image. Leveraging these two innovations we further employ a simple video fusion technique to encourage smooth transitions for long video animation. Empirical results demonstrate the superiority of our method over baseline approaches on two benchmarks. Notably our approach outperforms the strongest baseline by over 38% in terms of video fidelity on the challenging TikTok dancing dataset. Code and model will be made available at https://showlab.github.io/magicanimate.

Human matting is a foundation task in image and video processing where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional guidance or improve the temporal consistency of a single instance across frames. We propose a new framework MaGGIe Masked Guided Gradual Human Instance Matting which predicts alpha mattes progressively for each human instances while maintaining the computational cost precision and consistency. Our method leverages modern architectures including transformer attention and sparse convolution to output all instance mattes simultaneously without exploding memory and latency. Although keeping constant inference costs in the multiple-instance scenario our framework achieves robust and versatile performance on our proposed synthesized benchmarks. With the higher quality image and video matting benchmarks the novel multi-instance synthesis approach from publicly available sources is introduced to increase the generalization of models in real-world scenarios. Our code and datasets are available at https://maggie-matt.github.io

In this era the success of large language models and text-to-image models can be attributed to the driving force of large-scale datasets. However in the realm of 3D vision while remarkable progress has been made with models trained on large-scale synthetic and real-captured object data like Objaverse and MVImgNet a similar level of progress has not been observed in the domain of human-centric tasks partially due to the lack of a large-scale human dataset. Existing datasets of high-fidelity 3D human capture continue to be mid-sized due to the significant challenges in acquiring large-scale high-quality 3D human data. To bridge this gap we present MVHumanNet a dataset that comprises multi-view human action sequences of 4500 human identities. The primary focus of our work is on collecting human data that features a large number of diverse identities and everyday clothing using a multi-view human capture system which facilitates easily scalable data collection. Our dataset contains 9000 daily outfits 60000 motion sequences and 645 million frames with extensive annotations including human masks camera parameters 2D and 3D keypoints SMPL/SMPLX parameters and corresponding textual descriptions. To explore the potential of MVHumanNet in various 2D and 3D visual tasks we conducted pilot studies on view-consistent action recognition human NeRF reconstruction text-driven view-unconstrained human image generation as well as 2D view-unconstrained human image and 3D avatar generation. Extensive experiments demonstrate the performance improvements and effective applications enabled by the scale provided by MVHumanNet. As the current largest-scale 3D human dataset we hope that the release of MVHumanNet data with annotations will foster further innovations in the domain of 3D human-centric tasks at scale.

The capacity of existing human keypoint localization models is limited by keypoint priors provided by the training data. To alleviate this restriction and pursue more general model this work studies keypoint localization from a different perspective by reasoning locations based on keypiont clues in text descriptions. We propose LocLLM the first Large-Language Model (LLM) based keypoint localization model that takes images and text instructions as inputs and outputs the desired keypoint coordinates. LocLLM leverages the strong reasoning capability of LLM and clues of keypoint type location and relationship in textual descriptions for keypoint localization. To effectively tune LocLLM we construct localization-based instruction conversations to connect keypoint description with corresponding coordinates in input image and fine-tune the whole model in a parameter-efficient training pipeline. LocLLM shows remarkable performance on standard 2D/3D keypoint localization benchmarks. Moreover incorporating language clues into the localization makes LocLLM show superior flexibility and generalizable capability in cross dataset keypoint localization and even detecting novel type of keypoints unseen during training.

The study of complex human interactions and group activities has become a focal point in human-centric computer vision. However progress in related tasks is often hindered by the challenges of obtaining large-scale labeled datasets from real-world scenarios. To address the limitation we introduce M3Act a synthetic data generator for multi-view multi-group multi-person human atomic actions and group activities. Powered by Unity Engine M3Act features multiple semantic groups highly diverse and photorealistic images and a comprehensive set of annotations which facilitates the learning of human-centered tasks across single-person multi-person and multi-group conditions. We demonstrate the advantages of M3Act across three core experiments. The results suggest our synthetic dataset can significantly improve the performance of several downstream methods and replace real-world datasets to reduce cost. Notably M3Act improves the state-of-the-art MOTRv2 on DanceTrack dataset leading to a hop on the leaderboard from 10th to 2nd place. Moreover M3Act opens new research for controllable 3D group activity generation. We define multiple metrics and propose a competitive baseline for the novel task. Our code and data are available at our project page: http://cjerry1243.github.io/M3Act.

Most existing attention prediction research focuses on salient instances like humans and objects. However the more complex interaction-oriented attention arising from the comprehension of interactions between instances by human observers remains largely unexplored. This is equally crucial for advancing human-machine interaction and human-centered artificial intelligence. To bridge this gap we first collect a novel gaze fixation dataset named IG comprising 530000 fixation points across 740 diverse interaction categories capturing visual attention during human observers' cognitive processes of interactions. Subsequently we introduce the zero-shot interaction-oriented attention prediction task (ZeroIA) which challenges models to predict visual cues for interactions not encountered during training. Thirdly we present the Interactive Attention model (IA) designed to emulate human observers' cognitive processes to tackle the ZeroIA problem. Extensive experiments demonstrate that the proposed IA outperforms other state-of-the-art approaches in both ZeroIA and fully supervised settings. Lastly we endeavor to apply interaction-oriented attention to the interaction recognition task itself. Further experimental results demonstrate the promising potential to enhance the performance and interpretability of existing state-of-the-art HOI models by incorporating real human attention data from IG and attention labels generated by IA.

Learning 3D human-object interaction relation is pivotal to embodied AI and interaction modeling. Most existing methods approach the goal by learning to predict isolated interaction elements e.g. human contact object affordance and human-object spatial relation primarily from the perspective of either the human or the object. Which underexploit certain correlations between the interaction counterparts (human and object) and struggle to address the uncertainty in interactions. Actually objects' functionalities potentially affect humans' interaction intentions which reveals what the interaction is. Meanwhile the interacting humans and objects exhibit matching geometric structures which presents how to interact. In light of this we propose harnessing these inherent correlations between interaction counterparts to mitigate the uncertainty and jointly anticipate the above interaction elements in 3D space. To achieve this we present LEMON (LEarning 3D huMan-Object iNteraction relation) a unified model that mines interaction intentions of the counterparts and employs curvatures to guide the extraction of geometric correlations combining them to anticipate the interaction elements. Besides the 3D Interaction Relation dataset (3DIR) is collected to serve as the test bed for training and evaluation. Extensive experiments demonstrate the superiority of LEMON over methods estimating each element in isolation. The code and dataset are available at https://yyvhang.github.io/LEMON/

3D human generation is increasingly significant in various applications. However the direct use of 2D generative methods in 3D generation often results in losing local details while methods that reconstruct geometry from generated images struggle with global view consistency. In this work we introduce Joint2Human a novel method that leverages 2D diffusion models to generate detailed 3D human geometry directly ensuring both global structure and local details. To achieve this we employ the Fourier occupancy field (FOF) representation enabling the direct generation of 3D shapes as preliminary results with 2D generative models. With the proposed high-frequency enhancer and the multi-view recarving strategy our method can seamlessly integrate the details from different views into a uniform global shape. To better utilize the 3D human prior and enhance control over the generated geometry we introduce a compact spherical embedding of 3D joints. This allows for an effective guidance of pose during the generation process. Additionally our method can generate 3D humans guided by textual inputs. Our experimental results demonstrate the capability of our method to ensure global structure local details high resolution and low computational cost simultaneously. More results and the code can be found on our project page at http://cic.tju.edu.cn/faculty/likun/projects/Joint2Human.

Large-scale text-to-image (T2I) diffusion models have showcased incredible capabilities in generating coherent images based on textual descriptions enabling vast applications in content generation. While recent advancements have introduced control over factors such as object localization posture and image contours a crucial gap remains in our ability to control the interactions between objects in the generated content. Well-controlling interactions in generated images could yield meaningful applications such as creating realistic scenes with interacting characters. In this work we study the problems of conditioning T2I diffusion models with Human-Object Interaction (HOI) information consisting of a triplet label (person action object) and corresponding bounding boxes. We propose a pluggable interaction control model called InteractDiffusion that extends existing pre-trained T2I diffusion models to enable them being better conditioned on interactions. Specifically we tokenize the HOI information and learn their relationships via interaction embeddings. A conditioning self-attention layer is trained to map HOI tokens to visual tokens thereby conditioning the visual tokens better in existing T2I diffusion models. Our model attains the ability to control the interaction and location on existing T2I diffusion models which outperforms existing baselines by a large margin in HOI detection score as well as fidelity in FID and KID. Project page: https://jiuntian.github.io/interactdiffusion.

The analysis of the ubiquitous human-human interactions is pivotal for understanding humans as social beings. Existing human-human interaction datasets typically suffer from inaccurate body motions lack of hand gestures and fine-grained textual descriptions. To better perceive and generate human-human interactions we propose Inter-X a currently largest human-human interaction dataset with accurate body movements and diverse interaction patterns together with detailed hand gestures. The dataset includes 11K interaction sequences and more than 8.1M frames. We also equip Inter-X with versatile annotations of more than 34K fine-grained human part-level textual descriptions semantic interaction categories interaction order and the relationship and personality of the subjects. Based on the elaborate annotations we propose a unified benchmark composed of 4 categories of downstream tasks from both the perceptual and generative directions. Extensive experiments and comprehensive analysis show that Inter-X serves as a testbed for promoting the development of versatile human-human interaction analysis. Our dataset and benchmark will be publicly available for research purposes.

A simple yet effective method for occlusion-robust 3D human mesh reconstruction from a single image is presented in this paper. Although many recent studies have shown the remarkable improvement in human mesh reconstruction it is still difficult to generate accurate meshes when person-to-person occlusion occurs due to the ambiguity of who a body part belongs to. To address this problem we propose an instance-aware contrastive learning scheme. Specifically joint features belonging to the target human are trained to be proximate with the anchor feature (i.e. feature extracted from the body center position). On the other hand anchor features of different human instances are forced to be far apart so that joint features of each person can be clearly distinguished from others. By interpreting the joint possession based on such contrastive learning scheme the proposed method easily understands the spatial occupancy of body parts for each person in a given image thus can reconstruct reliable human meshes even with severely overlapped cases between multiple persons. Experimental results on benchmark datasets demonstrate the robustness of the proposed method compared to previous approaches under person-to-person occlusions. The code and model are publicly available at: https://github.com/DCVL-3D/InstanceHMR_release.

We investigate a new task in human motion prediction which is predicting motions under unexpected physical perturbation potentially involving multiple people. Compared with existing research this task involves predicting less controlled unpremeditated and pure reactive motions in response to external impact and how such motions can propagate through people. It brings new challenges such as data scarcity and predicting complex interactions. To this end we propose a new method capitalizing differentiable physics and deep neural networks leading to an explicit Latent Differentiable Physics (LDP) model. Through experiments we demonstrate that LDP has high data efficiency outstanding prediction accuracy strong generalizability and good explainability. Since there is no similar research a comprehensive comparison with 11 adapted baselines from several relevant domains is conducted showing LDP outperforming existing research both quantitatively and qualitatively improving prediction accuracy by as much as 70% and demonstrating significantly stronger generalization.

Understanding human motion from video is essential for a range of applications including pose estimation mesh recovery and action recognition. While state-of-the-art methods predominantly rely on transformer-based architectures these approaches have limitations in practical scenarios. Transformers are slower when sequentially predicting on a continuous stream of frames in real-time and do not generalize to new frame rates. In light of these constraints we propose a novel attention-free spatiotemporal model for human motion understanding building upon recent advancements in state space models. Our model not only matches the performance of transformer-based models in various motion understanding tasks but also brings added benefits like adaptability to different video frame rates and enhanced training speed when working with longer sequence of keypoints. Moreover the proposed model supports both offline and real-time applications. For real-time sequential prediction our model is both memory efficient and several times faster than transformer-based approaches while maintaining their high accuracy.

Transformers have been successfully applied in the field of video-based 3D human pose estimation. However the high computational costs of these video pose transformers (VPTs) make them impractical on resource-constrained devices. In this paper we present a plug-and-play pruning-and-recovering framework called Hourglass Tokenizer (HoT) for efficient transformer-based 3D human pose estimation from videos. Our HoT begins with pruning pose tokens of redundant frames and ends with recovering full-length tokens resulting in a few pose tokens in the intermediate transformer blocks and thus improving the model efficiency. To effectively achieve this we propose a token pruning cluster (TPC) that dynamically selects a few representative tokens with high semantic diversity while eliminating the redundancy of video frames. In addition we develop a token recovering attention (TRA) to restore the detailed spatio-temporal information based on the selected tokens thereby expanding the network output to the original full-length temporal resolution for fast inference. Extensive experiments on two benchmark datasets (i.e. Human3.6M and MPI-INF-3DHP) demonstrate that our method can achieve both high efficiency and estimation accuracy compared to the original VPT models. For instance applying to MotionBERT and MixSTE on Human3.6M our HoT can save nearly 50% FLOPs without sacrificing accuracy and nearly 40% FLOPs with only 0.2% accuracy drop respectively. Code and models are available at https://github.com/NationalGAILab/HoT.

Reconstructing 3D clothed human involves creating a detailed geometry of individuals in clothing with applications ranging from virtual try-on movies to games. To enable practical and widespread applications recent advances propose to generate a clothed human from an RGB image. However they struggle to reconstruct detailed and robust avatars simultaneously. We empirically find that the high-frequency (HF) and low-frequency (LF) information from a parametric model has the potential to enhance geometry details and improve robustness to noise respectively. Based on this we propose HiLo namely clothed human reconstruction with high- and low-frequency information which contains two components. 1) To recover detailed geometry using HF information we propose a progressive HF Signed Distance Function to enhance the detailed 3D geometry of a clothed human. We analyze that our progressive learning manner alleviates large gradients that hinder model convergence. 2) To achieve robust reconstruction against inaccurate estimation of the parametric model by using LF information we propose a spatial interaction implicit function. This function effectively exploits the complementary spatial information from a low-resolution voxel grid of the parametric model. Experimental results demonstrate that HiLo outperforms the state-of-the-art methods by 10.43% and 9.54% in terms of Chamfer distance on the Thuman2.0 and CAPE datasets respectively. Additionally HiLo demonstrates robustness to noise from the parametric model challenging poses and various clothing styles.

We have recently seen tremendous progress in photo-real human modeling and rendering. Yet efficiently rendering realistic human performance and integrating it into the rasterization pipeline remains challenging. In this paper we present HiFi4G an explicit and compact Gaussian-based approach for high-fidelity human performance rendering from dense footage. Our core intuition is to marry the 3D Gaussian representation with non-rigid tracking achieving a compact and compression-friendly representation. We first propose a dual-graph mechanism to obtain motion priors with a coarse deformation graph for effective initialization and a fine-grained Gaussian graph to enforce subsequent constraints. Then we utilize a 4D Gaussian optimization scheme with adaptive spatial-temporal regularizers to effectively balance the non-rigid prior and Gaussian updating. We also present a companion compression scheme with residual compensation for immersive experiences on various platforms. It achieves a substantial compression rate of approximately 25 times with less than 2MB of storage per frame. Extensive experiments demonstrate the effectiveness of our approach which significantly outperforms existing approaches in terms of optimization speed rendering quality and storage overhead.

Human-centric 3D scene understanding has recently drawn increasing attention driven by its critical impact on robotics. However human-centric real-life scenarios are extremely diverse and complicated and humans have intricate motions and interactions. With limited labeled data supervised methods are difficult to generalize to general scenarios hindering real-life applications. Mimicking human intelligence we propose an unsupervised 3D detection method for human-centric scenarios by transferring the knowledge from synthetic human instances to real scenes. To bridge the gap between the distinct data representations and feature distributions of synthetic models and real point clouds we introduce novel modules for effective instance-to-scene representation transfer and synthetic-to-real feature alignment. Remarkably our method exhibits superior performance compared to current state-of-the-art techniques achieving 87.8% improvement in mAP and closely approaching the performance of fully supervised methods (62.15 mAP vs. 69.02 mAP) on HuCenLife Dataset.

Recent advances in neural rendering have improved both training and rendering times by orders of magnitude. While these methods demonstrate state-of-the-art quality and speed they are designed for photogrammetry of static scenes and do not generalize well to freely moving humans in the environment. In this work we introduce Human Gaussian Splats (HUGS) that represents an animatable human together with the scene using 3D Gaussian Splatting (3DGS). Our method takes only a monocular video with a small number of (50-100) frames and it automatically learns to disentangle the static scene and a fully animatable human avatar within 30 minutes. We utilize the SMPL body model to initialize the human Gaussians. To capture details that are not modeled by SMPL (e.g cloth hairs) we allow the 3D Gaussians to deviate from the human body model. Utilizing 3D Gaussians for animated humans brings new challenges including the artifacts created when articulating the Gaussians. We propose to jointly optimize the linear blend skinning weights to coordinate the movements of individual Gaussians during animation. Our approach enables novel-pose synthesis of human and novel view synthesis of both the human and the scene. We achieve state-of-the-art rendering quality with a rendering speed of 60 FPS while being 100x faster to train over previous work.

3D hand-object interaction data is scarce due to the hardware constraints in scaling up the data collection process. In this paper we propose HOIDiffusion for generating realistic and diverse 3D hand-object interaction data. Our model is a conditional diffusion model that takes both the 3D hand-object geometric structure and text description as inputs for image synthesis. This offers a more controllable and realistic synthesis as we can specify the structure and style inputs in a disentangled manner. HOIDiffusion is trained by leveraging a diffusion model pre-trained on large-scale natural images and a few 3D human demonstrations. Beyond controllable image synthesis we adopt the generated 3D data for learning 6D object pose estimation and show its effectiveness in improving perception systems. Project page: https://mq-zhang1.github.io/HOIDiffusion.