Splatshot: 3D Face Avatar Generation from a Single Unconstrained Photo

• URL: http://arxiv.org/abs/2606.01493v1
• Date: Sun, 31 May 2026 23:19:44 GMT
• Title: Splatshot: 3D Face Avatar Generation from a Single Unconstrained Photo
• Abstract summary: We present SplatShot, a training-free framework that couples explicit 3D representations directly within the denoising process.<n>Experiments on diverse in-the-wild images demonstrate that SplatShot produces 3D avatars with superior identity preservation, photorealism, and multi-view consistency.
• Score: 22.10478800887373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstructing a photorealistic 3D face avatar from a single unconstrained photograph is challenging: feed-forward 3D Gaussian Splatting (3DGS) models degrade on out-of-distribution inputs, while pretrained diffusion models produce high-fidelity images but lack multi-view consistency. We observe that these paradigms are fundamentally complementary: explicit 3D representations guarantee geometric consistency, whereas 2D diffusion priors ensure photorealism. Building on this, we propose SplatShot, a training-free framework that couples these representations directly within the denoising process. Given a base 3DGS face model and a single reference image, we jointly denoise all target views using a per-step 3D feedback loop. At each timestep, we predict clean images from the noisy latents, refit the 3DGS to these multi-view predictions, and back-propagate the photometric discrepancy between the 3DGS re-renderings and 2D predictions into the noise estimate. This steers the sampling trajectory toward strictly 3D-coherent, identity-faithful outputs. Experiments on diverse in-the-wild images demonstrate that SplatShot produces 3D avatars with superior identity preservation, photorealism, and multi-view consistency.

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