RMAA Video AI: Continuous Spatiotemporal Vector Manifolds, Douglas-Peucker Topological Invariance, and Non-Raster Video Synthesis

A unified mathematical critique and foundational architectural solution for generative video intelligence. Modern diffusion video models (e.g. DiT, Sora, Runway, Kling) suffer from rubbery morphing artifacts, limb hallucinations, spatial incoherence, and catastrophic computational costs caused by treating continuous dynamic scenes as discrete grids of stochastic RGB pixels. RMAA Video AI introduces a non-raster, continuous spatiotemporal vector architecture. By representing video as topologically invariant closed polygon manifolds (Douglas-Peucker contour approximations), quantized chromatic centroid fields, and affine Lie algebra trajectory splines, RMAA Video AI achieves: (1) Topological invariance—physical entities cannot dissolve, morph, or exhibit unnatural deformations; (2) True infinite-resolution rendering (1080p, 4K, 8K) natively executing on hardware-accelerated 2D canvas at native screen dpi with zero upscaling blur; (3) Extreme orders-of-magnitude bandwidth reduction via the custom binary SVGV container codec; (4) The world's first open-source Spatiotemporal Vector-Video dataset blueprint hosted at rmaapk/vector.