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arxiv: 2007.08547 · v1 · pith:GOPKVDLNnew · submitted 2020-07-16 · 💻 cs.CV · cs.GR

Talking-head Generation with Rhythmic Head Motion

classification 💻 cs.CV cs.GR
keywords headmotionvideoachievesembeddinggeneratemodulemovements
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When people deliver a speech, they naturally move heads, and this rhythmic head motion conveys prosodic information. However, generating a lip-synced video while moving head naturally is challenging. While remarkably successful, existing works either generate still talkingface videos or rely on landmark/video frames as sparse/dense mapping guidance to generate head movements, which leads to unrealistic or uncontrollable video synthesis. To overcome the limitations, we propose a 3D-aware generative network along with a hybrid embedding module and a non-linear composition module. Through modeling the head motion and facial expressions1 explicitly, manipulating 3D animation carefully, and embedding reference images dynamically, our approach achieves controllable, photo-realistic, and temporally coherent talking-head videos with natural head movements. Thoughtful experiments on several standard benchmarks demonstrate that our method achieves significantly better results than the state-of-the-art methods in both quantitative and qualitative comparisons. The code is available on https://github.com/ lelechen63/Talking-head-Generation-with-Rhythmic-Head-Motion.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MMTalker: Multiresolution 3D Talking Head Synthesis with Multimodal Feature Fusion

    cs.CV 2026-04 unverdicted novelty 6.0

    MMTalker combines multi-resolution mesh sampling with residual graph convolutions and dual cross-attention to synthesize accurate 3D talking head motions from audio.