The field of computer vision and graphics is moving towards more efficient and accurate methods for dynamic scene reconstruction and compression. Recent developments have focused on novel view synthesis, motion-aware neural rendering, and sparse multi-view dynamic Gaussian Splatting. These approaches have shown promising results in reconstructing dynamic scenes from monocular videos and improving the quality of reconstructed images. Noteworthy papers include: 4D3R, which introduces a pose-free dynamic neural rendering framework that achieves up to 1.8dB PSNR improvement over state-of-the-art methods. Splatography, which presents an approach that splits the canonical Gaussians and deformation field into foreground and background components, producing state-of-the-art qualitative and quantitative results. Modulo Video Recovery via Selective Spatiotemporal Vision Transformer, which demonstrates the first deep learning framework for modulo video reconstruction, achieving state-of-the-art performance in modulo video recovery. Burst Image Quality Assessment, which proposes a new task of Burst Image Quality Assessment to evaluate the task-driven quality of each frame within a burst sequence. Machines Serve Human, which sets out a novel collaborative compression method based on the machine-vision-oriented compression. Neural B-frame Video Compression with Bi-directional Reference Harmonization, which proposes a novel NBVC method with the proposed Bi-directional Motion Converge and Bi-directional Contextual Fusion.