Hyperbolic Contrastive Diffusion Transformer for Posthuman Subjectivity Representation in Generative Animation

This work introduces the Hyperbolic Contrastive Diffusion Transformer (HC-DiT) to address the challenges of modeling hierarchical evolution and semantic misalignment in generative animation of posthuman subjectivity. It constructs a Poincaré ball manifold to mitigate dimensional crowding and capture nonlinear evolutionary structures. A hyperbolic contrastive learning loss and a multilingual semantic alignment module are integrated to enhance cross-modal consistency. The diffusion transformer backbone, featuring spatiotemporal attention, generates coherent animation sequences. Experiments on a 20K dataset demonstrate that HC-DiT achieves a Hyperbolic Distortion Rate of 0.0865 and a Subjectivity Logic Score of 4.81, outperforming ViTGAN and vanilla DiT. Ablation studies further validate the efficacy of hyperbolic constraints and semantic alignment. This work presents a novel pattern recognition framework for philosophical visual generation.