A single diffusion model jointly generates portrait RGB images and aligned depth maps, and its fine-tuned variants can estimate depth, edit from depth, relight, and produce audio-driven talking heads with depth.
SPACE: Speech-driven Portrait Animation with Controllable Expression
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abstract
Animating portraits using speech has received growing attention in recent years, with various creative and practical use cases. An ideal generated video should have good lip sync with the audio, natural facial expressions and head motions, and high frame quality. In this work, we present SPACE, which uses speech and a single image to generate high-resolution, and expressive videos with realistic head pose, without requiring a driving video. It uses a multi-stage approach, combining the controllability of facial landmarks with the high-quality synthesis power of a pretrained face generator. SPACE also allows for the control of emotions and their intensities. Our method outperforms prior methods in objective metrics for image quality and facial motions and is strongly preferred by users in pair-wise comparisons. The project website is available at https://deepimagination.cc/SPACE/
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Joint Learning of Depth and Appearance for Portrait Image Animation
A single diffusion model jointly generates portrait RGB images and aligned depth maps, and its fine-tuned variants can estimate depth, edit from depth, relight, and produce audio-driven talking heads with depth.