Due to the interference of background noise, existing video anomaly detection methods are prone to detect some normal events in complex scenes as anomalies. Meanwhile, we note that the diversity of normal patterns has not been adequately considered, i.e., the normal events that are worthy of reference in the test data have not been properly utilized, which raises the risk of missed and false detections. In this work, we combine the tasks of next-frame prediction and predicted-frame reconstruction to propose a noise-resistant video anomaly detection method. For the prediction task, we develop an RGB Error-Guided Multiscale Predictive Coding (EG-MPC) framework to overcome the interference of background noise on the learning of appearance and motion features of objects at various scales, thus achieving high-quality frame prediction. For the reconstruction task, we introduce the Dynamic Memory Modules (DMMs) into the reconstruction network, and design sparse aggregation and selective update strategies for the memory items in the DMMs to effectively represent diverse normal patterns while increasing the difficulty of reconstructing anomalies, thus making it easier to distinguish between normal and abnormal frames. Extensive experiments on four benchmark datasets demonstrate that our proposed method outperforms state-of-the-art approaches, especially in complex scenarios.

Text-to-motion has widespread applications in VR/AR, games, filmmaking, and robotics. In real-world scenarios, humans frequently initiate interactions with the surrounding environment using specific body parts. In this work, we introduce BOCO, a novel method for fine-grained BOdy part COntrol in text-driven human motion synthesis with interactive intention. BOCO employs a decoupled strategy to extract body part movements and leverages large language models to capture corresponding part-level semantics described in text. Additionally, BOCO introduces a spatial coherence module based on a part-based graph structure to ensure semantic accuracy and spatial consistency. We developed a specialized dataset from HUMANML3D for training and evaluation. Experiments demonstrate that BOCO significantly enhances nuanced, accurate, and adaptable text-to-motion generation. By focusing on human-centric interactive intentions, BOCO is better suited for general and flexible interaction scenarios, paving the way for broader interaction applications as object inference and simulation technologies advance.

With the improvement of computing power and the rapid development of deep learning, deep-learning-based methods are widely used in the field of seismic data noise suppression. Supervised learning has proven to be effective but its performance largely relies on noise-free data labeling, which is often unavailable or an expensive process. Therefore, as a form of unsupervised learning, self-supervised learning emerged to overcome this difficulty, with its labels coming from the training dataset itself. In this letter, we propose a self-supervised learning method that requires only raw seismic data to train the model by using the Noise2Noise strategy, which takes advantage of the unpredictability of noises to regress from noisy data to clean data. Our method aims at improving the noise suppression effect for common-reflection-point (CRP) gathers. By comparing with conventional methods, both synthetic and field data show that the proposed framework is not only effective in suppressing random noise, but also remains effective for coherent noise.