The distribution of pedestrian trajectories is highly complex and influenced by the scene, nearby pedestrians, and subjective intentions. This complexity presents challenges for modeling and generalizing trajectory prediction. Previous methods modeled the feature space of future trajectories based on the high-dimensional feature space of historical trajectories, but this approach is suboptimal because it overlooks the similarity between historical and future trajectories. Our proposed method, TrajCLIP, utilizes contrastive learning and idempotent generative networks to address this issue. By pairing historical and future trajectories and applying contrastive learning on the encoded feature space, we enforce same-space consistency constraints. To manage complex distributions, we use idempotent loss and tightness loss to control over-expansion in the latent space. Additionally, we have developed a trajectory interpolation algorithm and synthetic trajectory data to enhance model capacity and improve generalization. Experimental results on public datasets demonstrate that TrajCLIP achieves state-of-the-art performance and excels in scene-to-scene transfer, few-shot transfer, and online learning tasks.

Trajectory prediction with uncertainty is a critical and challenging task for autonomous driving. Nowadays, we can easily access sensor data represented in multiple views. However, cross-view consistency has not been evaluated by the existing models, which might lead to divergences between the multimodal predictions from different views. It is not practical and effective when the network does not comprehend the 3D scene, which could cause the downstream module in a dilemma. Instead, we predicts multimodal trajectories while maintaining cross-view consistency. We presented a cross-view trajectory prediction method using shared 3D Queries (XVTP3D). We employ a set of 3D queries shared across views to generate multi-goals that are cross-view consistent. We also proposed a random mask method and coarse-to-fine cross-attention to capture robust cross-view features. As far as we know, this is the first work that introduces the outstanding top-down paradigm in BEV detection field to a trajectory prediction problem. The results of experiments on two publicly available datasets show that XVTP3D achieved state-of-the-art performance with consistent cross-view predictions.

Realistic 3D facial modeling and animation have been increasingly used in many graphics, animation, and virtual reality applications. However, generating realistic fine-scale wrinkles on 3D faces, in particular, on animated 3D faces, is still a challenging problem that is far away from being resolved. In this article we propose an end-to-end system to automatically augment coarse-scale 3D faces with synthesized fine-scale geometric wrinkles. By formulating the wrinkle generation problem as a supervised generation task, we implicitly model the continuous space of face wrinkles via a compact generative model, such that plausible face wrinkles can be generated through effective sampling and interpolation in the space. We also introduce a complete pipeline to transfer the synthesized wrinkles between faces with different shapes and topologies. Through many experiments, we demonstrate our method can robustly synthesize plausible fine-scale wrinkles on a variety of coarse-scale 3D faces with different shapes and expressions.

Abstract Virtualized traffic via various simulation models and real‐world traffic data are promising approaches to reconstruct detailed traffic flows. A variety of applications can benefit from the virtual traffic, including, but not limited to, video games, virtual reality, traffic engineering and autonomous driving. In this survey, we provide a comprehensive review on the state‐of‐the‐art techniques for traffic simulation and animation. We start with a discussion on three classes of traffic simulation models applied at different levels of detail. Then, we introduce various data‐driven animation techniques, including existing data collection methods, and the validation and evaluation of simulated traffic flows. Next, we discuss how traffic simulations can benefit the training and testing of autonomous vehicles. Finally, we discuss the current states of traffic simulation and animation and suggest future research directions.

Most of existing traffic simulation methods have been focused on simulating vehicles on freeways or city-scale urban networks. However, relatively little research has been done to simulate intersectional traffic to date despite its broad potential applications. In this paper, we propose a novel deep learning-based framework to simulate and edit intersectional traffic. Specifically, based on an in-house collected intersectional traffic dataset, we employ the combination of convolution network (CNN) and recurrent network (RNN) to learn the patterns of vehicle trajectories in intersectional traffic. Besides simulating novel intersectional traffic, our method can be used to edit existing intersectional traffic. Through many experiments as well as comparative user studies, we demonstrate that the results by our method are visually indistinguishable from ground truth, and our method can outperform existing methods.

Trajectory prediction for objects is challenging and critical for various applications (e.g., autonomous driving, and anomaly detection). Most of the existing methods focus on homogeneous pedestrian trajectories prediction, where pedestrians are treated as particles without size. However, they fall short of handling crowded vehicle-pedestrian-mixed scenes directly since vehicles, limited with kinematics in reality, should be treated as rigid, non-particle objects ideally. In this paper, we tackle this problem using separate LSTMs for heterogeneous vehicles and pedestrians. Specifically, we use an oriented bounding box to represent each vehicle, calculated based on its position and orientation, to denote its kinematic trajectories. We then propose a framework called VP-LSTM to predict the kinematic trajectories of both vehicles and pedestrians simultaneously. In order to evaluate our model, a large dataset containing the trajectories of both vehicles and pedestrians in vehicle-pedestrian-mixed scenes is specially built. Through comparisons between our method with state-of-the-art approaches, we show the effectiveness and advantages of our method on kinematic trajectories prediction in vehicle-pedestrian-mixed scenes.

Human trajectory prediction is challenging and critical in various applications (e.g., autonomous vehicles and social robots). Because of the continuity and foresight of the pedestrian movements, the moving pedestrians in crowded spaces will consider both spatial and temporal interactions to avoid future collisions. However, most of the existing methods ignore the temporal correlations of interactions with other pedestrians involved in a scene. In this work, we propose a Spatial-Temporal Graph Attention network (STGAT), based on a sequence-to-sequence architecture to predict future trajectories of pedestrians. Besides the spatial interactions captured by the graph attention mechanism at each time-step, we adopt an extra LSTM to encode the temporal correlations of interactions. Through comparisons with state-of-the-art methods, our model achieves superior performance on two publicly available crowd datasets (ETH and UCY) and produces more "socially" plausible trajectories for pedestrians.