Research map / Video / Video generation
VividFace: A Robost and High-Fidelity Video Face Swapping Framework
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Proposes VividFace, a diffusion-based framework for video face swapping that uses a hybrid training strategy to improve temporal consistency and robustness in real-world scenarios.
Problem
Existing face swapping methods are primarily trained on static images and struggle with temporal consistency and complex real-world video scenarios.
Approach
A diffusion-based video face swapping framework trained with a novel hybrid training strategy, claimed as the first dedicated video face swapping framework.
Closest papers by meaning
- Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality (CVPR 2026 · similarity 0.91)
- FaithfulFaces: Pose-Faithful Facial Identity Preservation for Text-to-Video Generation (NeurIPS 2026 · similarity 0.88)
- StableAnimator: High-Quality Identity-Preserving Human Image Animation (CVPR 2025 · similarity 0.86)
- ControlFace: Harnessing Facial Parametric Control for Face Rigging (CVPR 2025 · similarity 0.86)
- DiffPortrait3D: Controllable Diffusion for Zero-Shot Portrait View Synthesis (CVPR 2024 · similarity 0.86)
- Enhancing Identity-Deformation Disentanglement in StyleGAN for One-Shot Face Video Re-Enactment (AAAI 2025 · similarity 0.86)
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