Generating diverse and natural human motion sequences based on textual descriptions constitutes a fundamental and challenging research area within the domains of computer vision, graphics, and robotics. Despite significant advancements in this field, current methodologies often face challenges regarding zero-shot generalization capabilities, largely attributable to the limited size of training datasets. Moreover, the lack of a comprehensive evaluation framework impedes the advancement of this task by failing to identify directions for improvement. In this work, we aim to push text-to-motion into a new era, that is, to achieve the generalization ability of zero-shot. To this end, firstly, we develop an efficient annotation pipeline and introduce MotionMillion-the largest human motion dataset to date, featuring over 2,000 hours and 2 million high-quality motion sequences. Additionally, we propose MotionMillion-Eval, the most comprehensive benchmark for evaluating zero-shot motion generation. Leveraging a scalable architecture, we scale our model to 7B parameters and validate its performance on MotionMillion-Eval. Our results demonstrate strong generalization to out-of-domain and complex compositional motions, marking a significant step toward zero-shot human motion generation. The code is available at https://github.com/VankouF/MotionMillion-Codes.

Neural rendering techniques, including NeRF and Gaussian Splatting (GS), rely on photometric consistency to produce high-quality reconstructions. However, in real-world driving scenarios, it is challenging to guarantee perfect photometric consistency in acquired images. Appearance codes have been widely used to address this issue, but their modeling capability is limited, as a single code is applied to the entire image. Recently, the bilateral grid was introduced to perform pixel-wise color mapping, but it is difficult to optimize and constrain effectively. In this paper, we propose a novel multi-scale bilateral grid that unifies appearance codes and bilateral grids. We demonstrate that this approach significantly improves geometric accuracy in dynamic, decoupled autonomous driving scene reconstruction, outperforming both appearance codes and bilateral grids. This is crucial for autonomous driving, where accurate geometry is important for obstacle avoidance and control. Our method shows strong results across four datasets: Waymo, NuScenes, Argoverse, and PandaSet. We further demonstrate that the improvement in geometry is driven by the multi-scale bilateral grid, which effectively reduces floaters caused by photometric inconsistency.

Previous works on object detection have achieved high accuracy in closed-set scenarios, but their performance in open-world scenarios is not satisfactory. One of the challenging open-world problems is corner case detection in autonomous driving. Existing detectors struggle with these cases, relying heavily on visual appearance and exhibiting poor generalization ability. In this paper, we propose a solution by reducing the discrepancy between known and unknown classes and introduce a multimodal-enhanced objectness notion learner. Leveraging both vision-centric and vision-language multiple modalities, our semi-supervised learning framework imparts objectness knowledge to the student model, enabling class-aware detection. Our approach, Multimodal-Enhanced Objectness Learner (MENOL) for Corner Case Detection, significantly improves recall for novel classes with lower training costs. By achieving a $76.6 \% \mathrm{mAR}$-corner and $79.8 \%$ mAR-agnostic on the CODA-val dataset with just 5100 labeled training images, MENOL outperforms the baseline ORE by $71.3 \%$ and $60.6 \%$, respectively. The code will be available at https://github.com/tryhiseyyysum/ MENOL.

Messenger RNA (mRNA) vaccines have shown great potential for anti-tumor therapy due to the advantages in safety, efficacy and industrial production. However, it remains a challenge to identify suitable cancer neoantigens that can be targeted for mRNA vaccines. Abnormal alternative splicing occurs in a variety of tumors, which may result in the translation of abnormal transcripts into tumor-specific proteins. High-throughput technologies make it possible for systematic characterization of alternative splicing as a source of suitable target neoantigens for mRNA vaccine development. Here, we summarized difficulties and challenges for identifying alternative splicing-derived cancer neoantigens from RNA-seq data and proposed a conceptual framework for designing personalized mRNA vaccines based on alternative splicing-derived cancer neoantigens. In addition, several points were presented to spark further discussion toward improving the identification of alternative splicing-derived cancer neoantigens.

The world is facing a pandemic of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Adaptive immune responses are essential for SARS-CoV-2 virus clearance. Although a large body of studies have been conducted to investigate the immune mechanism in COVID-19 patients, we still lack a comprehensive understanding of the BCR repertoire in patients. In this study, we used the single-cell V(D)J sequencing to characterize the BCR repertoire across convalescent COVID-19 patients. We observed that the BCR diversity was significantly reduced in disease compared with healthy controls. And BCRs tend to skew toward different V gene segments in COVID-19 and healthy controls. The CDR3 sequences of heavy chain in clonal BCRs in patients were more convergent than that in healthy controls. In addition, we discovered increased IgG and IgA isotypes in the disease, including IgG1, IgG3 and IgA1. In all clonal BCRs, IgG isotypes had the most frequent class switch recombination events and the highest somatic hypermutation rate, especially IgG3. Moreover, we found that an IgG3 cluster from different clonal groups had the same IGHV, IGHJ and CDR3 sequences (IGHV4-4-CARLANTNQFYDSSSYLNAMDVW-IGHJ6). Overall, our study provides a comprehensive characterization of the BCR repertoire in COVID-19 patients, which contributes to the understanding of the mechanism for the immune response to SARS-CoV-2 infection.