#computer-vision

A Hacker News discussion highlighted LoGeR, a Google DeepMind and UC Berkeley project that uses hybrid memory to scale dense 3D reconstruction across extremely long videos without post-hoc optimization.

Highlighted in r/MachineLearning, VeridisQuo fuses an EfficientNet-B4 spatial stream with FFT and DCT frequency features, then uses GradCAM remapping to show which facial regions triggered a deepfake prediction.

A well-received r/MachineLearning post introduced VeridisQuo, an open-source deepfake detector that fuses spatial and frequency-domain signals and overlays GradCAM heatmaps onto manipulated video frames. The project stands out because the author shared concrete architecture and training details instead of just a demo clip.

Google DeepMind introduced D4RT, a single model framework for dynamic 4D scene reconstruction and tracking. The company reports up to 300x efficiency gains versus prior methods, highlighting real-time potential for robotics and AR workloads.