{"ID":3045296,"CreatedAt":"2026-06-04T00:20:49.549693789Z","UpdatedAt":"2026-06-05T12:00:18.729514559Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2605.25220","arxiv_id":"2605.25220","title":"Multi-view Consistent 3D Gaussian Head Avatars 'without' Multi-view Generation","abstract":"High-fidelity 3D Gaussian head avatar generation is critical for applications such as AR/VR, telepresence, and digital humans. Existing methods depend on multi-view datasets, 3D captures, or intermediate 2D view synthesis. In contrast, we learn both conditional and unconditional 3D head models from randomly sampled 2D images alone, without using multi-view data, 3D supervision, or intermediate view generation. We introduce MVCHead, a single-shot state space model that enforces multi-view consistency (MVC) directly in the 3D representation while regressing 3D Gaussians under these constraints. At its core, we propose a Hierarchical State Space (HiSS) block that progressively refines Gaussians from coarse to fine, while capturing long-range dependencies. Within each HiSS block, we modify Mamba's standard unidirectional scan with the proposed Hierarchical Bi-directional State Scan (HiBiSS) that aligns recurrence with the axes along which multi-view inconsistencies are strongest. Finally, we design an SE(3) Multi-view Critic that judges whether a set of self-renders arises from a single underlying 3D configuration, rewarding cross-view pixel alignment without observing real multi-view pairs. MVCHead achieves state-of-the-art perceptual quality, surpasses prior methods in both texture and geometric consistency, and maintains comparable shape consistency. To demonstrate scalability, we release FaceGS-10K, the first large-scale dataset of ready-to-use 3D Gaussian head assets for training and evaluation of 3D head models. Project Page and code: https://humansensinglab.github.io/MVCHead/","short_abstract":"High-fidelity 3D Gaussian head avatar generation is critical for applications such as AR/VR, telepresence, and digital humans. Existing methods depend on multi-view datasets, 3D captures, or intermediate 2D view synthesis. In contrast, we learn both conditional and unconditional 3D head models from randomly sampled 2D...","url_abs":"https://arxiv.org/abs/2605.25220","url_pdf":"https://arxiv.org/pdf/2605.25220v1","authors":"[\"Aviral Chharia\",\"Fernando De la Torre\"]","published":"2026-05-24T19:09:15Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.GR\",\"cs.RO\"]","methods":"[]","has_code":false}