Aligning the rhythm of visual motion in a video with a given music track is a practical need in multimedia production, yet remains an underexplored task in autonomous video editing. Effective alignment between motion and musical beats enhances viewer engagement and visual appeal, particularly in music videos, promotional content, and cinematic editing. Existing methods typically depend on labor-intensive manual cutting, speed adjustments, or heuristic-based editing techniques to achieve synchronization. While some generative models handle joint video and music generation, they often entangle the two modalities, limiting flexibility in aligning video to music beats while preserving the full visual content. In this paper, we propose a novel and efficient framework-termed MVAA (Music-Video Auto-Alignment)-that automatically edits video to align with the rhythm of a given music track while preserving the original visual content. To enhance flexibility, we modularize the task into a two-step process in our MVAA: aligning motion keyframes with audio beats, followed by rhythm-aware video inpainting. Specifically, we first insert keyframes at timestamps aligned with musical beats, then use a frame-conditioned diffusion model to generate coherent intermediate frames, preserving the original video's semantic content. Since comprehensive test-time training can be time-consuming, we adopt a two-stage strategy: pretraining the inpainting module on a small video set to learn general motion priors, followed by rapid inference-time fine-tuning for video-specific adaptation. This hybrid approach enables adaptation within ~10 minutes with one epoch on a single NVIDIA 4090 GPU using CogVideoX-5b-I2V [77] as the backbone. Extensive experiments show that our approach can achieve high-quality beat alignment and visual smoothness. User studies further validate the natural rhythmic quality of the results, confirming their effectiveness for practical music-video editing. The code is available at: zhangxinyu-xyz.github.io/MVAA

From virtual game to physical robot, games have witnessed the development of artificial intelligence (AI) technology, especially the data-driven technology represented by deep learning. Compared with virtual games, a physical robot game such as RoboMaster AI challenge needs to build a complete closed-loop architecture composed of perception, planning, control, and decision-making to support autonomous confrontation. Perception, as the eye of the robot, its performance in the complex environment depends on a massive dataset. Although there are many open perception datasets, these datasets are difficult to meet the needs of RoboMaster AI challenge due to the high dynamics of the task, the distinctiveness of the objects, and limited computing resources. In this paper, we release a dataset named Neurons11Neurons is a team dedicated to promoting the development of robot with deep neural network. We will release the code and dataset at https://github.com/DRL-CASIA/NeuronsDataset. perception dataset for RoboMaster AI challenge, which covers 3 tasks including monocular depth estimation, lightweight object detection, and multi-view 3D object detection, and makes up the data blank in this field. In addition, we also evaluate State-Of-The-Art (SOTA) methods on each task, hoping to provide an impartial benchmark for the development of perception algorithm.