Meta and Oxford's VGGT-Omega is a CVPR 2026 oral that cuts GPU training memory by roughly 70%, scales to 10B parameters, and beats optimization pipelines on dynamic scenes. Here's what changed and how I'd evaluate it before betting a product on feed-forward 3D.
Security researcher Bill Swearingen trained reinforcement-learning patterns that block ALPR and surveillance detection without hiding from video. At DefCon he wrapped a Toyota Yaris and drove past a Flock camera. Here is what builders on both sides should learn.
Meshy T2 uses flow matching to generate vertices and connectivity in parallel, not autoregressive mesh tokens. Median image-to-mesh latency is six seconds with controllable face budgets and native multi-part output.
VLM3 matches expert 3D vision models on depth, correspondence, and pose with three tricks: focal length unification, text pixel refs, and data scaling. No custom loss required.
Most 3D body tracking stacks need Python and PyTorch at runtime. SAM3DBody-cpp wraps Meta's SAM 3D Body model in a standalone C++ engine with ONNX Runtime, outputting 70 joints and full meshes from a camera feed.