CtrlRecon: Steering Expressive Human Reconstruction toward Prescribed States

Expressive human pose and shape estimation (EHPS) increasingly underpins digital-human systems by mapping visual inputs to the poses, hand articulations, body shape and facial expressions enacted by an avatar. If an adversary can control these outputs, an innocuous input could induce attacker-specified behavior absent from the source, elevating the threat from reconstruction degradation to deliberate behavioral manipulation. Yet existing attacks against EHPS remain predominantly untargeted that reconstructions can be corrupted or driven toward implausible configurations, but not whether an adversary can steer them toward a prescribed state. To address this gap, we introduce CtrlRecon, a framework for targeted attacks on EHPS. Specifically, CtrlRecon constructs an image-specific target by integrating attacker-specified articulations with the corresponding clean predictions, holding it constant throughout the optimization process. Guided by this static target, a representation-aware objective jointly enforces the prescribed modifications and preserves the unmodified components. Subsequently, a two-stage procedure optimizes over the bounded continuous space and refines the result into the discrete 8-bit domain, ensuring adversarial effectiveness is fully maintained after standard image export. Experiments across three EHPS estimators validate the effectiveness of CtrlRecon for targeted control. In VLM-based evaluation, CtrlRecon receives 60.10% of the votes, exceeding the strongest baseline by 36.68 percentage points.