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GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians
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GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians
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We introduce GaussianAvatars, a new method to create photorealistic head avatars that are fully controllable in terms of expression, pose, and viewpoint. The core idea is a dynamic 3D representation based on 3D Gaussian splats that are rigged to a parametric morphable face model. This combination facilitates photorealistic rendering while allowing for precise animation control via the underlying parametric model, e.g., through expression transfer from a driving sequence or by manually changing the morphable model parameters. We parameterize each splat by a local coordinate frame of a triangle and optimize for explicit displacement offset to obtain a more accurate geometric representation. During avatar reconstruction, we jointly optimize for the morphable model parameters and Gaussian splat parameters in an end-to-end fashion. We demonstrate the animation capabilities of our photorealistic avatar in several challenging scenarios. For instance, we show reenactments from a driving video, where our method outperforms existing works by a significant margin.
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Cited by 5 Pith papers
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EmbodiedHead introduces a Rectified-Flow Diffusion Transformer with differentiable renderer and single-stream listening-speaking conditioning to achieve real-time high-fidelity conversational avatars.
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S-Avatar: Diffusion-Guided Gaussian Head Avatars from a Single Image
A three-stage pipeline generates animatable 3D Gaussian head avatars from one image by diffusion-based splat synthesis, FLAME fitting, and inverse-distance binding with scale adaptation.
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