The authors introduce Polycepta, an object-centric appearance state estimation framework that reformulates appearance modeling as a recursive estimation problem. Unlike traditional methods relying on static, frame-independent descriptors, Polycepta constructs and continuously updates independent appearance states for each tracked object. This approach allows future representations to be estimated from accumulated observations rather than memorizing them through a specific learning strategy. A key feature is that appearance estimation quality improves progressively as object states evolve during inference. The framework enables appearance estimation for unseen classes by encouraging the learning of object-specific representation construction. Extensive experiments on KITTI, Waymo Open Dataset, and MOT17 demonstrate consistent reductions in identity switches and improved tracking performance. When integrated into the RobMOT framework, Polycepta operates at 90.57 Hz and achieves a MOTA of 92.27% on the KITTI benchmark.