One4D: Unified 4D Generation and Reconstruction via Decoupled LoRA Control

A new framework for articulated object reconstruction has been proposed, utilizing planar Gaussian Splatting to reconstruct 3D objects from sparse-view RGB images captured from a single state. This method introduces a Gaussian information field to optimize viewpoint selection and employs a coarse-to-fine optimization strategy for depth estimation and part segmentation.

A new dataset named MM-UAV has been introduced, designed for tracking unmanned aerial vehicles (UAVs) using a multi-modal approach that includes RGB, infrared, and event signals. This dataset features over 30 challenging scenarios with 1,321 synchronized sequences and more than 2.8 million annotated frames, addressing the limitations of single-modality tracking in difficult conditions.

MambaRefine-YOLO has been introduced as a dual-modality small object detector specifically designed for Unmanned Aerial Vehicle (UAV) imagery, addressing the challenges of low resolution and background clutter in small object detection. The model incorporates a Dual-Gated Complementary Mamba fusion module (DGC-MFM) and a Hierarchical Feature Aggregation Neck (HFAN), achieving a state-of-the-art mean Average Precision (mAP) of 83.2% on the DroneVehicle dataset.

The recent introduction of ShowMe, a unified framework for instructional image and video generation, addresses the limitations of previous methods that treated image manipulation and video prediction as separate tasks. By activating spatial and temporal components of video diffusion models, ShowMe enhances the generation of visual instructions in interactive world simulators.

A new framework called KTCAA has been introduced for few-shot cross-modal sketch person re-identification, aiming to bridge the gap between hand-drawn sketches and RGB surveillance images. This framework addresses challenges related to domain discrepancy and perturbation invariance, proposing innovative components like Alignment Augmentation and Knowledge Transfer Catalyst to enhance model robustness and alignment capabilities.

A new framework called IF-Edit has been introduced, leveraging large-scale video diffusion models for zero-shot image editing. This method addresses challenges such as prompt misalignment and blurry late-stage frames, enhancing the capabilities of pretrained models for instruction-driven image editing.

The introduction of the Promptable 3D Detector (Pro3D) marks a significant advancement in roadside monocular 3D detection, which involves identifying objects in RGB frames and predicting their 3D attributes, such as bird's-eye-view locations. This innovation leverages 2D detections as prompts to enhance the accuracy and efficiency of 3D detection processes.

LinVideo has been introduced as a post-training framework that enhances video generation efficiency by replacing certain self-attention modules with linear attention, addressing the quadratic computational costs associated with traditional video diffusion models. This method preserves the original model's performance while significantly reducing resource demands.