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Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis
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One-shot 3D talking portrait generation aims to reconstruct a 3D avatar from an unseen image, and then animate it with a reference video or audio to generate a talking portrait video. The existing methods fail to simultaneously achieve the goals of accurate 3D avatar reconstruction and stable talking face animation. Besides, while the existing works mainly focus on synthesizing the head part, it is also vital to generate natural torso and background segments to obtain a realistic talking portrait video. To address these limitations, we present Real3D-Potrait, a framework that (1) improves the one-shot 3D reconstruction power with a large image-to-plane model that distills 3D prior knowledge from a 3D face generative model; (2) facilitates accurate motion-conditioned animation with an efficient motion adapter; (3) synthesizes realistic video with natural torso movement and switchable background using a head-torso-background super-resolution model; and (4) supports one-shot audio-driven talking face generation with a generalizable audio-to-motion model. Extensive experiments show that Real3D-Portrait generalizes well to unseen identities and generates more realistic talking portrait videos compared to previous methods. Video samples and source code are available at https://real3dportrait.github.io .
Forward citations
Cited by 17 Pith papers
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Splatshot: 3D Face Avatar Generation from a Single Unconstrained Photo
SplatShot is a training-free method that inserts per-step 3DGS refitting and photometric feedback into diffusion denoising to enforce multi-view consistency for single-photo 3D face avatars.
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Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling
Talker-T2AV achieves better lip-sync accuracy, video quality, and audio quality than dual-branch baselines by separating high-level shared autoregressive modeling from modality-specific low-level diffusion refinement ...
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UIKA: Fast Universal Head Avatar from Pose-Free Images
UIKA is a feed-forward animatable Gaussian head model using UV-guided correspondence estimation and learnable UV tokens with dual-level attention, trained on large-scale synthetic data to handle pose-free inputs.
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Instant Expressive Gaussian Head Avatars at Over 100 FPS
A single-photo avatar encoder with per-Gaussian feature-space deformation animates faces at 107 FPS with expression quality competitive with diffusion models.
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FantasyPortrait: Enhancing Multi-Character Portrait Animation with Expression-Augmented Diffusion Transformers
A DiT-based portrait animation model transfers implicit facial expressions to one or more characters using a masked cross-attention mechanism, supported by a new multi-face dataset and benchmark.
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FFAvatar: Feed-Forward 4D Head Avatar Reconstruction from Sparse Portrait Images
A Transformer 3D-Gaussian model reconstructs incremental, animatable 4D head avatars from sparse portraits via alternating attention, sparse-to-dense UV densification, and residual motion refinement.
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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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Real-Time Generation of Streamable Talking Portrait Video with Reference-Guided Deep Compression VAEs
A causal VAE with variable reference guidance and a Rectified Flow Transformer enables real-time streamable high-quality talking portrait video generation from audio and images.
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SplitAvatar: One-shot Head Avatar with Autoregressive Gaussian Splitting
SplitAvatar applies an autoregressive graph splitting network with mesh topology extension and gated density control to generate detailed one-shot head avatars via 3D Gaussian Splatting.
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SDTalk: Structured Facial Priors and Dual-Branch Motion Fields for Generalizable Gaussian Talking Head Synthesis
SDTalk proposes a generalizable one-shot 3DGS talking head method that uses structured facial priors for complete reconstruction and dual-branch motion fields for dynamics, outperforming prior identity-specific approaches.
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FlexAvatar: Learning Complete 3D Head Avatars with Partial Supervision
FlexAvatar introduces bias sinks in a transformer to unify monocular and multi-view training, yielding complete 3D head avatars with strong generalization and view extrapolation from single images.
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THEval. Evaluation Framework for Talking Head Video Generation
THEval proposes eight metrics for evaluating talking head videos on quality, naturalness, and synchronization, tested on 85,000 videos from 17 models with a new curated dataset.
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Mask-Free Audio-driven Talking Face Generation for Enhanced Visual Quality and Identity Preservation
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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JOLT3D: Joint Learning of Talking Heads and 3DMM Parameters with Application to Lip-Sync
JOLT3D jointly trains a 3DMM reconstruction network with a talking head generator, then uses FACS mouth blendshapes from a diffusion model to lip-sync videos while preserving the original chin contour.
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FA-LAM: Focus-Aware Large Avatar Model for One-Shot 4D Animatable Gaussian Head
A one-shot model for animatable 3D/4D Gaussian head reconstruction that adds attention regularization, decoupled reconstruction-animation training, and autoregressive visibility-gated fusion, reporting consistent metr...
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Livatar-1: Real-Time Talking Heads Generation with Tailored Flow Matching
Livatar claims the best lip-sync score in its comparison table and real-time throughput, but the preprint does not describe the model or release any code, data, or weights.
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