Kinematic Energy Guidance for Anatomically Valid Hand Synthesis in Portrait Diffusion

Abstract

The synthesis of anatomically valid human hands remains a significant challenge in contemporary text-to-image diffusion models. Despite the remarkable progress in high-fidelity portrait generation, models frequently produce severe structural anomalies such as poly-dactyly, missing digits, and biomechanically impossible joint articulations. This paper introduces a novel framework, Kinematic Energy Guidance, designed to enforce strict anatomical constraints during the diffusion sampling process without requiring architectural modifications or domain-specific retraining. By modeling the human hand as a highly constrained articulated kinematic chain, we formulate an energy-based penalty function that evaluates the biomechanical feasibility of generated skeletal structures in latent space. This kinematic energy function computes the divergence of predicted joint angles and positional configurations from a normative human anatomical prior. The gradient of this energy is then injected into the reverse diffusion process, actively steering the generative trajectory toward manifolds corresponding to anatomically plausible configurations. Comprehensive evaluations on large-scale human portrait datasets demonstrate that the proposed method significantly reduces the incidence of anatomical distortions while preserving the photorealism and textual alignment of the underlying foundation model. The results establish a new state-of-the-art paradigm for physically constrained image synthesis, bridging the gap between unconstrained generative priors and deterministic biomechanical laws.