SplatCtrlA: Generalizable Single Image to Fully Controllable 3D Avatar

Expressive human avatar creation from a single image ishighly challenging due to the inherently ill-posed nature of the prob-lem, as well as the complexities of appearance preservation and dynamichuman modeling. To address these challenges, this work presents a com-prehensive pipeline for training a large feed-forward model that efficientlygenerates fully controllable 3D avatars from single-image. Due to thescarcity of consistent training data, we construct a large-scale 3D Gaus-sian avatar dataset to support model training. To better recover appear-ance details, we propose an input-aware decoding scheme that fully lever-ages information from the input image. Furthermore, to achieve compre-hensive full-body control, we introduce a Gaussian blending–based facialenhancement module and apply Gaussian geometric constraints to sta-bilize expressive avatar generation. Extensive experiments demonstratethat our method enables one-shot reconstruction of photorealistic avatarswith whole-body control, outperforming existing works in terms of hu-man dynamic and appearance details.