TAVR: Generate Your Talking Avatar from Video Reference

TAVR is a talking-avatar generation framework from HeyGen Research and NTU that uses short video references rather than a single image to improve identity preservation across poses, expressions, lighting conditions, and target scenes. Built on the Wan2.1-T2V-14B video diffusion backbone, it supports flexible reference lengths, filters identity-relevant visual tokens, combines target and reference information through adapted self-attention, and uses audio cross-attention for lip synchronization. Its long-video approach carries motion information between clips and anchors appearance to reduce identity drift, while a three-stage training process progresses from same-scene learning to cross-scene fine-tuning and identity-focused reinforcement learning. The authors also introduced a 158-pair cross-scene benchmark designed to test avatar consistency across distinct environments, reporting that TAVR achieved the highest overall quality score of 16.42 compared with 14.13 for the next-best method, with identity similarity improving as reference frames increased without reducing lip-sync or general visual quality. The work supports HeyGen’s video-reference avatar product and notes that the platform applies consent verification for digital-twin creation.

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