FlexiAvatar: Unified 3D Gaussian Human Avatars Under Arbitrary Body Visibility

Reconstructing animatable 3D human avatars from monocularvideo is a fundamental problem in computer vision with broad applicationsin AR/VR and digital content creation. Existing approaches typically coupleparametric body models with neural rendering or 3D Gaussian splattingand optimize all body regions jointly from short videos, which often de-grades fidelity in the visible areas. To overcome this limitation, we introduceFlexiAvatar, a unified framework that explicitly optimizes only the visiblebody regions, effectively eliminating artifacts arising from unobserved limbs.Our method integrates occlusion-robust SMPL-X tracking with part-specificresidual refinement to capture high-frequency geometric and appearancedetails. To complete entirely unseen regions (e.g., back views), we leverage adiffusion-based approach to generate texture consistent with the observedappearance. Experiments on full-body (NeuMan, ZJU-MoCap, WildAvatar),upper/half-body (talk-show clips), and head-only (INSTA) inputs show thatFlexiAvatar delivers consistently higher reconstruction quality, outperformingstate-of-the-art methods by an average PSNR improvement of approximately3% across datasets. Finally, by restricting optimization to observed regions,our method reduces the effective number of Gaussians that must be opti-mized and rendered, leading to reduced runtime and memory overhead inpartial-visibility scenarios. The project page can be found here.