20 accepted papers
In multi-view 3D human pose estimation, models typically rely on images captured simultaneously from different camera views to predict a pose at a specific moment. While providing accurate spatial information, this traditional approach often overlooks the rich temporal dependencies between adjacent…
This paper introduces a new control signal for facial motion generation: timeline control. Compared to audio and text signals, timelines provide more fine-grained control, such as generating specific facial motions with precise timing. Users can specify a multi-track timeline of facial actions arran…
Research in visual perception has shown that the human visual system utilizes high-level feedback information to guide lower-level processing, enabling adaptation to signals of varying characteristics. Inspired by this, we propose the Feedback multi-Level feature Extractor (Flex) to dynamically adju
Recent advances in large vision-language models (LVLMs) typically employ vision encoders based on the Vision Transformer (ViT) architecture. The division of the images into patches by ViT results in a fragmented perception, thereby hindering the visual understanding capabilities of LVLMs. In this pa…
Embodied scene understanding requires not only comprehending visual-spatial information that has been observed but also determining where to explore next in the 3D physical world. Existing 3D Vision-Language (3D-VL) models primarily focus on grounding objects in static observations from 3D reconstru…
Single point supervised oriented object detection has gained attention and made initial progress within the community. Diverse from those approaches relying on one-shot samples or powerful pretrained models (e.g. SAM), PointOBB has shown promise due to its prior-free feature. In this paper, we propo…
Existing skeleton spatial-temporal models tend to deteriorate the positional distinguishability of skeleton joints and lead to inaccurate spatial matching and poor interpretability. This paper proposes a novel contrastive diffusion graph convolutional network (CD-GCN) for few-shot action recognition…
Point clouds play an important role in 3D analysis, which has broad applications in robotics and autonomous driving. The pre-training fine-tuning paradigm has shown great potential in the point cloud domain. Full fine-tuning is generally effective but leads to a heavy storage and computational burde…
This paper presents Action-SGFA, a novel action feature alignment approach to learn unified joint embeddings across four action modalities incorporating scene graph (SG) comprehension. A new training paradigm for Action-SGFA is also devised to improve pre-trained VL models using datasets with SG ann…
This study explores unsupervised open-vocabulary skeleton action recognition, aiming at addressing inaccurate spatial matching and poor interpretability of existing GCN models. We present Skeleton-DGCFA, an approach to make feature alignment (FA) of skeleton with image modalities based on a large pr…
3D vision-language grounding (3D-VL) is an emerging field that aims to connect the 3D physical world with natural language, which is crucial for achieving embodied intelligence. Current 3D-VL models rely heavily on sophisticated modules, auxiliary losses, and optimization tricks, which calls for a s…
Dynamic scene graphs generated from video clips could help enhance the semantic visual understanding in a wide range of challenging tasks such as environmental perception, autonomous navigation, and task planning of self-driving vehicles and mobile robots. In the process of temporal and spatial mode…
Anomaly detection could be applied in a wide range of fields from industrial scene to medical imaging analysis. Although invertible flow models are developed to accomplish unsupervised anomaly detection, they are usually hard to train and have limited capabilities of accurately modeling the distribu…
Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To t…
Existing permutation-invariant methods can be divided into two categories according to the aggregation scope, i.e. global aggregation and local one. Although the global aggregation methods, e. g., PointNet and Deep Sets, get involved in simpler structures, their performance is poorer than the local…
In environmental perception of autonomous driving, zero-shot semantic segmentation that can make prediction of new categories without using any labeled training samples is considered as a challenging task. One key step in this task is to transfer knowledge across categories via auxiliary semantic wo…
Great progress has been made on estimating disparity maps from stereo images. However, with the limited stereo data available in the existing datasets and unstable ranging precision of current stereo methods, industry-level stereo matching in autonomous driving remains challenging. In this paper, we…
Object detection in videos has drawn increasing attention since it is more practical in real scenarios. Most of the deep learning methods use CNNs to process each decoded frame in a video stream individually. However, the free of charge yet valuable motion information already embedded in the video c…
Video objection detection is challenging in the presence of appearance deterioration in certain video frames. One of typical solutions is to enhance per-frame features through aggregating neighboring frames. But the features of objects are usually not spatially calibrated across frames due to motion…
Disparity estimation for binocular stereo images finds a wide range of applications. Traditional algorithms may fail on featureless regions, which could be handled by high-level clues such as semantic segments. In this paper, we suggest that appropriate incorporation of semantic cues can greatly rec…