18 accepted papers
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
Neural Radiance Fields (NeRF)-based Visual Simultaneous Localization and Mapping (SLAM) achieve superior scene geometric modeling and robust camera tracking by leveraging neural representations. Existing methods typically relied on multi-resolution hash encoding with truncated signed distance field
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
Audio-visual speech recognition (AVSR) typically improves recognition accuracy in noisy environments by integrating noise-immune visual cues with audio signals. Nevertheless, high-noise audio inputs are prone to introducing adverse interference into the feature fusion process. To mitigate this, rece…
We introduce physics-as-representation, a learning paradigm that encodes physical structure and geometric laws into visual representations, enabling models to see the unseen—the underlying 3D geometry and motion dynamics not apparent in raw pixels. We instantiate this paradigm in gaze perception by …
While large language-image pre-trained models like CLIP offer powerful generic features for image clustering, existing methods typically freeze the encoder. This creates a fundamental mismatch between the model
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
The analysis of facial micro-expressions (MEs) has emerged as a significant application and topic within the field of image and video processing. However, challenges persist due to the brief duration and subtle intensity of these spontaneous expressions. This paper presents a novel multi-prior fusio…
3D Gaze estimation is a challenging task due to two main issues. First, existing methods focus on analyzing dense features (e.g., large pixel regions), which are sensitive to local noise (e.g., light spots, blurs) and result in increased computational complexity. Second, an eyeball model can corresp…
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…
Despite the rapid development of neural vocoders in recent years, they usually suffer from some intrinsic challenges like opaque modeling, and parameter-performance trade-off. In this study, we propose an innovative time-frequency (T-F) domain-based neural vocoder to resolve the above-mentioned chal
Visual speech recognition (VSR), commonly known as lip reading, has garnered significant attention due to its wide-ranging practical applications. The advent of deep learning techniques and advancements in hardware capabilities have significantly enhanced the performance of lip reading models. Despi…
In recent years, the rapid advancement of large language models (LLMs) has significantly reshaped the landscape of scientific research. While LLMs have achieved notable success across various domains, their application in specialized fields such as lunar exploration remains underdeveloped, and their…
Inertial tracking (IT), independent of the environment and external infrastructure, has long been the ideal solution for providing location services to humans. Despite significant strides in inertial tracking empowered by deep learning, prevailing neural inertial tracking predominantly utilizes conv…
The use of dry electrode electroencephalography (EEG) systems holds significant importance in advancing the everyday application of emotion recognition. However, adapting it to real-world applications faces unique challenges due to low signal-to-noise ratios and unreliable emotion labels. To address…
Lip reading, the process of interpreting silent speech from visual lip movements, has gained rising attention for its wide range of realistic applications. Deep learning approaches greatly improve current lip reading systems. However, lip reading in cross-speaker scenarios where the speaker identity…
The task of vision-and-language navigation in continuous environments (VLN-CE) aims at training an autonomous agent to perform low-level actions to navigate through 3D continuous surroundings using visual observations and language instructions. The significant potential of VLN-CE for mobile robots h
Cross-modal alignment is one key challenge for Vision-and-Language Navigation (VLN). Most existing studies concentrate on mapping the global instruction or single sub-instruction to the corresponding trajectory. However, another critical problem of achieving fine-grained alignment at the entity leve…