Generating identity-consistent videos without 'copy-paste' issues? EchoVideo shows how.
The paper introduces EchoVideo, a novel method for identity-preserving video generation. It tackles the "copy-paste" artifacts and low identity similarity issues in existing methods by using high-level semantic features and a two-stage training strategy.
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Paper - https://arxiv.org/abs/2501.13452
Original Problem 😥:
→ Current identity-preserving video generation methods often produce "copy-paste" artifacts.
→ They also suffer from low similarity between generated and input faces.
→ These issues arise from over-reliance on low-level facial image information.
→ This leads to rigid facial expressions and irrelevant details in generated videos.
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Solution in this Paper 💡:
→ EchoVideo uses an Identity Image-Text Fusion Module (IITF).
→ IITF integrates high-level semantic features from text and facial images.
→ This captures clean facial identity representations.
→ IITF discards irrelevant details like occlusions and lighting variations from input images.
→ EchoVideo employs a two-stage training strategy.
→ The second stage randomly uses shallow facial information.
→ This balances fidelity enhancement from shallow features and reduces over-reliance on them.
→ This encourages the model to use high-level features for robust identity representation.
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Key Insights from this Paper 🤔:
→ Relying solely on low-level facial image information causes "copy-paste" artifacts and limits identity similarity.
→ Fusing high-level semantic features from both text and facial images improves identity preservation.
→ A two-stage training process that balances shallow and deep features enhances video quality and identity consistency.
→ Pre-fusion integration of multimodal information simplifies learning within Diffusion Transformer (DiT) models.
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