28 accepted papers
Controllable hand image generation aims to synthesize geometrically accurate images with consistent appearance. Recently, diffusion models have been widely applied for hand image synthesis. However, through input-level fusion or feature-level modulation, existing methods inject control signals with
With the rise of pre-trained vision-language models such as CLIP, performing video anomaly detection (VAD) through cross-modal reasoning has become an emerging trend. However, we observe that CLIP still suffers from weak abnormality awareness: normal and abnormal descriptions are highly entangled in
Understanding interacting hand-object from monocular videos is crucial for immersive and dexterous interactions in AR/VR and robotic applications. However, existing monocular reconstruction methods primarily assume rigid grasping and static object geometry. When applied to articulated manipulations,
In this paper, we explore video anomaly detection (VAD) from a fine-grained perspective, which aims not only to detect anomalous events but also to identify their specific categories. Due to the limited number of examples per category, existing methods either fail to handle intra-class variation acr
3D hand pose estimation (HPE) from sparse inertial measurement units (IMUs) has shown great potential in human-computer interaction. However, due to the significant semantic gap between sparse local motion information and structured global pose information, estimating hand poses from sparse IMU sign
Electromyography (EMG)-based gesture recognition has emerged as a promising approach for human-computer interaction. However, its performance is often limited by the scarcity of labeled EMG data, significant cross-user variability, and poor generalization to unseen gestures. To address these challen
Online micro gesture recognition from hand skeletons is critical for VR/AR interaction but faces challenges due to limited public datasets and task-specific algorithms. Micro gestures involve subtle motion patterns, which make constructing datasets with precise skeletons and frame-level annotations
Manually annotating accurate 3D hand poses is extremely time-consuming and labor-intensive. Existing self-supervised hand pose estimation methods leverage the discrepancy between input images and rendered outputs, or multi-view consistency constraints, as the driving force to optimize networks and p
Precise 3D hand posture is essential for learning musical instruments. Reconstructing highly precise 3D hand gestures enables learners to correct and master proper techniques through 3D simulation and Extended Reality. However, exsiting methods typically rely on precisely calibrated multi-camera sys
Large Vision-Language Models (LVLMs) pretrained on large-scale multimodal data have shown promising capabilities in Video Anomaly Detection (VAD). However, their ability to reason about abnormal events based on scene semantics remains underexplored. In this paper, we investigate LVLMs’ behavior in V…
Large Vision-Language Models (LVLMs) have a significant issue with object hallucinations, where researchers have noted that LVLMs often mistakenly determine objects as present in images where they do not actually exist. Some recent studies evaluate the occurrence of object hallucinations by asking L…
Recent advances in hand-object interaction modeling have employed implicit representations, such as Signed Distance Functions (SDF) and Neural Radiance Fields (NeRF) to reconstruct hands and objects with arbitrary topology and photo-realistic detail. However, these methods often rely on dense 3D sur…
In recent years, skeleton-based action recognition has gained significant attention due to its robustness in varying environmental conditions. However, most existing methods struggle to distinguish fine-grained actions due to subtle motion features, minimal inter-class variation, and they often fail…
3D hand reconstruction is essential in non-contact human-computer interaction applications, but existing methods struggle with low-resolution images, which occur in slightly distant interactive scenes. Leveraging temporal information can mitigate the limitations of individual low-resolution images t…
3D hand pose estimation plays a critical role in various human-computer interaction tasks. Single-frame 3D hand pose estimation methods have poor temporal smoothness and are easily affected by self-occlusion, which severely impacts their practical applicability. Traditional joint-based sequential po…
Multi-view 3D human pose estimation (HPE) leverages complementary information across views to improve accuracy and robustness. Traditional methods rely on camera calibration to establish geometric correspondences, which is sensitive to calibration accuracy and lacks flexibility in dynamic settings.…
Existing research in multi-hop questions has identified two reasoning modes: latent reasoning and factual shortcuts, but has not deeply investigated how these modes differ during inference. This impacts both model generalization ability and downstream reasoning tasks. In this work, we systematically
"Recent research has explored implicit representations, such as signed distance function (SDF), for interacting hand-object reconstruction. SDF enables modeling hand-held objects with arbitrary topology and overcomes the resolution limitations of parametric models, allowing for finer-grained reconst…
Previous 3D hand pose estimation methods primarily rely on a single modality, either RGB or depth, and the comprehensive utilization of the dual modalities has not been extensively explored. RGB and depth data provide complementary information and thus can be fused to enhance the robustness of 3D ha…
ecent progress in video anomaly detection suggests that the features of appearance and motion play crucial roles in distinguishing abnormal patterns from normal ones. However we note that the effect of spatial scales of anomalies is ignored. The fact that many abnormal events occur in limited locali…
Hand Pose Estimation (HPE) is a crucial technique for human-computer interaction perception. Recent works have shown that leveraging temporal information yields significant importance in the stability of the HPE system. However, existing sequential optimization methods are mainly designed for specif…
Sustainable cities requires high-quality community management and surveillance analytics, which are supported by video anomaly detection techniques. However, mainstream video anomaly detection techniques still require manually labeled data and do not apply to real-world massive videos. Without label…
Reconstructing interacting hands from a single RGB image is a very challenging task. On the one hand, severe mutual occlusion and similar local appearance between two hands confuse the extraction of visual features, resulting in the misalignment of estimated hand meshes and the image. On the other h…
Recent advancements in 3D hand pose estimation have shown promising results, but its effectiveness has primarily relied on the availability of large-scale annotated datasets, the creation of which is a laborious and costly process. To alleviate the label-hungry limitation, we propose a self-supervis…
RGB and depth modalities provide complementary information, which can be effectively utilized to improve the performance of hand detection in the wild. Most existing fusion-based methods model the channel-wise or spatial-wise cross-modal correlation to exploit the complementary RGB-D information, in…
Depth images and point clouds are the two most commonly used data representations for depth-based 3D hand pose estimation. Benefiting from the structuring of image data and the inherent inductive biases of the 2D Convolutional Neural Network (CNN), image-based methods are highly efficient and effect…
"Temporal grounding aims to locate a target video moment that semantically corresponds to the given sentence query in an untrimmed video. However, recent works find that existing methods suffer a severe temporal bias problem. These methods do not reason the target moment locations based on the visua…
In this work, we study the cross-view information fusion problem in the task of self-supervised 3D hand pose estimation from the depth image. Previous methods usually adopt a hand-crafted rule to generate pseudo labels from multi-view estimations in order to supervise the network training in each vi…