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This paper introduces SpatialAvatar-0, a novel approach for generating high-quality 4D head avatars using a shared Gaussian representation that integrates feed-forward generation and per-subject refinement. By employing a parameter-free K-source mean-pool and a layout-preserving refinement loop, the method achieves significant improvements in performance while drastically reducing the required training iterations. The results demonstrate that SpatialAvatar-0 outperforms existing state-of-the-art methods, achieving higher PSNR on cross-domain benchmarks without prior training on those domains.
Surpassing existing benchmarks, SpatialAvatar-0 achieves superior 4D head avatar quality with up to 60x fewer training iterations than traditional methods.
High-quality 4D head avatars from one or a few source portraits are central to telepresence, AR/VR, and digital-human interaction. 3D Gaussian Splatting (3DGS) has emerged as the dominant representation, with two complementary regimes (generalizable feed-forward predictors and per-subject refiners) maturing in parallel. However, existing feed-forward predictors are trained on a single dataset family with a hard-coded source count, inheriting the corresponding domain bias. Per-subject refiners require 300K--600K iterations and rely on adaptive densification that destroys upstream Gaussian layouts, preventing the two regimes from sharing a representation end-to-end. To bridge both regimes we propose SpatialAvatar-0 on a shared FLAME-mesh-bound Gaussian representation: a feed-forward generator with a parameter-free K-source mean-pool and a monocular-temporal to multi-view-spatial two-phase schedule that anchors against identity-prior collapse onto the smaller multi-view set. We further introduce a 10K-iter layout-preserving per-subject refinement loop that freezes the FLAME-binding and Gaussian count and replaces densification with a three-component anti-spike regularization. On VFHQ/HDTF cross-domain zero-shot we surpass the in-domain leader GAGAvatar by +1.5 dB PSNR despite never training on either test domain, and on the SplattingAvatar monocular benchmark we lead every reported metric, surpassing the 300K-iter GeoAvatar by +1.3 dB PSNR at up to 60x shorter per-subject schedule than common SOTA baselines. Website: https://spatialwalk.github.io/SpatialAvatar-0.
MIT CSAIL