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GPAvatar: Generalizable and Precise Head Avatar from Image(s)
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Head avatar reconstruction, crucial for applications in virtual reality, online meetings, gaming, and film industries, has garnered substantial attention within the computer vision community. The fundamental objective of this field is to faithfully recreate the head avatar and precisely control expressions and postures. Existing methods, categorized into 2D-based warping, mesh-based, and neural rendering approaches, present challenges in maintaining multi-view consistency, incorporating non-facial information, and generalizing to new identities. In this paper, we propose a framework named GPAvatar that reconstructs 3D head avatars from one or several images in a single forward pass. The key idea of this work is to introduce a dynamic point-based expression field driven by a point cloud to precisely and effectively capture expressions. Furthermore, we use a Multi Tri-planes Attention (MTA) fusion module in the tri-planes canonical field to leverage information from multiple input images. The proposed method achieves faithful identity reconstruction, precise expression control, and multi-view consistency, demonstrating promising results for free-viewpoint rendering and novel view synthesis.
Forward citations
Cited by 8 Pith papers
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FFAvatar: Feed-Forward 4D Head Avatar Reconstruction from Sparse Portrait Images
FFAvatar uses a Transformer-based 3D Gaussian model with alternating attention and sparse-to-dense learning to enable feed-forward, incremental reconstruction of animatable 4D head avatars from sparse portrait images.
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MF-Talk, a mask-free and identity-reference-free three-stage pipeline, improves visual quality and identity preservation in talking-face generation while remaining competitive on lip-sync.
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Low-Rank Head Avatar Personalization with Registers
A Register Module, a learnable 3D feature space rigged to a 3DMM mesh, improves LoRA-based personalization of head avatars by teaching the model to focus on identity-specific DINOv2 features during adaptation.
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Total-Editing: Head Avatar with Editable Appearance, Motion, and Lighting
Total-Editing is a unified 3D head avatar framework that separately controls appearance, motion, and lighting through an intrinsically decomposed neural radiance field, and reports stronger identity, expression, pose,...
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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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Split and Drive: Dual-Axis Disentanglement for Real-Time Gaussian Head Avatars
A single-image 3DGS head avatar with internalized motion encoding and three region-specialized Gaussian branches runs real-time end-to-end and matches or beats recent baselines on reenactment metrics.
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SyncTalk++: High-Fidelity and Efficient Synchronized Talking Heads Synthesis Using Gaussian Splatting
SyncTalk++ synthesizes speech-driven talking-head videos via 3D Gaussian Splatting and reports state-of-the-art synchronization and quality at up to 101 FPS.
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