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CyberHost: Taming Audio-driven Avatar Diffusion Model with Region Codebook Attention
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Diffusion-based video generation technology has advanced significantly, catalyzing a proliferation of research in human animation. However, the majority of these studies are confined to same-modality driving settings, with cross-modality human body animation remaining relatively underexplored. In this paper, we introduce, an end-to-end audio-driven human animation framework that ensures hand integrity, identity consistency, and natural motion. The key design of CyberHost is the Region Codebook Attention mechanism, which improves the generation quality of facial and hand animations by integrating fine-grained local features with learned motion pattern priors. Furthermore, we have developed a suite of human-prior-guided training strategies, including body movement map, hand clarity score, pose-aligned reference feature, and local enhancement supervision, to improve synthesis results. To our knowledge, CyberHost is the first end-to-end audio-driven human diffusion model capable of facilitating zero-shot video generation within the scope of human body. Extensive experiments demonstrate that CyberHost surpasses previous works in both quantitative and qualitative aspects.
Forward citations
Cited by 7 Pith papers
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AgentHOI: Multi-Agent Reasoning for Human-Object-Interaction Video Generation via Implicit Representation Alignment
AgentHOI generates human-object interaction videos from text plus one human image and one object image, using multi-agent action planning and implicit text-to-motion feature alignment inside a video diffusion model.
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HunyuanVideo-HOMA: Generic Human-Object Interaction in Multimodal Driven Human Animation
HunyuanVideo-HOMA generates human-object interaction videos from weak, sparse inputs: one arm pose, an object center dot, a human photo, and an object photo.
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InfinityHuman: Towards Long-Term Audio-Driven Human
A coarse-to-fine audio-driven animation framework that uses pose-guided refinement and hand-specific reward learning to generate long, identity-stable talking videos.
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EDTalk++: Full Disentanglement for Controllable Talking Head Synthesis
EDTalk++ disentangles talking-head video into four orthogonal motion banks (mouth, pose, eyes, expression) and drives them from either video or audio inputs.
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FixTalk: Taming Identity Leakage for High-Quality Talking Head Generation in Extreme Cases
FixTalk adds two modules to a real-time GAN talking-head model, decoupling identity from motion to stop identity leakage while using a memory to recover details and reduce artifacts.
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Multi-View Face and Gesture Animation with Dynamic Gaussians
Combining separate face and hand models with a parametric body and Gaussian splatting enables multi-view-consistent upper-body avatars that can be re-animated with new expressions and gestures.
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LLIA -- Enabling Low-Latency Interactive Avatars: Real-Time Audio-Driven Portrait Video Generation with Diffusion Models
Using consistency distillation, INT8 quantization, and pipeline parallelism, the LLIA system generates portrait video from audio at 78 FPS, with 140 ms initial latency.
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