That's What I Said: Fully-Controllable Talking Face Generation
원문 초록
The goal of this paper is to synthesise talking faces with controllable facial motions. To achieve this goal, we propose two key ideas. The first is to establish a canonical space where every face has the same motion patterns but different identities. The second is to navigate a multimodal motion space that only represents motion-related features while eliminating identity information. To disentangle identity and motion, we introduce an orthogonality constraint between the two different latent spaces. From this, our method can generate natural-looking talking faces with fully controllable facial attributes and accurate lip synchronisation. Extensive experiments demonstrate that our method achieves state-of-the-art results in terms of both visual quality and lip-sync score. To the best of our knowledge, we are the first to develop a talking face generation framework that can accurately manifest full target facial motions including lip, head pose, and eye movements in the generated video without any additional supervision beyond RGB video with audio.
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JoyGen: Audio-Driven 3D Depth-Aware Talking-Face Video Editing
DisentTalk: Cross-lingual Talking Face Generation via Semantic Disentangled Diffusion Model
UniSync: A Unified Framework for Audio-Visual Synchronization
PC-Talk: Precise Facial Animation Control for Audio-Driven Talking Face Generation
Playmate: Flexible Control of Portrait Animation via 3D-Implicit Space Guided Diffusion
STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking
토론
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