HumanForge: A Human-Centric Deepfake Video Benchmark with Multi-Agent Forgery Rationales
Rapid advancements in video diffusion models and temporal editing tools have enabled the generation of highly realistic human-centric videos, posing unprecedented challenges to digital content forensics. Existing benchmarks primarily focus on either face-swapping or global text-to-video synthesis, overlooking the crucial dimensions of human-object or human-human interactions and multi-modal alignment. To address these limitations, we introduce HumanForge, a unified, large-scale, and multi-paradigm human-centric video forgery dataset. To construct and annotate this dataset without labor-intensi
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- PossiblePossibly related (embedding) · 50%hacksider/Deep-Live-Cam →
- PossiblePossibly related (embedding) · 47%deepseek-ai/DeepSeek-V3-0324 →
- LinkedLinked via arxiv author · 85%Wenbo Xu →
“HumanForge: A Human-Centric Deepfake Video Benchmark with Multi-Agent Forgery Rationales”
- LinkedLinked via arxiv author · 85%Zhimin Chen →
“HumanForge: A Human-Centric Deepfake Video Benchmark with Multi-Agent Forgery Rationales”
- LinkedLinked via arxiv author · 85%Xiaojie Liang →
“HumanForge: A Human-Centric Deepfake Video Benchmark with Multi-Agent Forgery Rationales”
- LinkedLinked via arxiv author · 85%Hengrui Liu →
“HumanForge: A Human-Centric Deepfake Video Benchmark with Multi-Agent Forgery Rationales”
- LinkedLinked via arxiv author · 85%Yawei Luo →
“HumanForge: A Human-Centric Deepfake Video Benchmark with Multi-Agent Forgery Rationales”
- PossiblePossibly related (embedding) · 54%FennelFetish/qapyq →