Joint Learning of Depth and Appearance for Portrait Image Animation

• URL: http://arxiv.org/abs/2501.08649v1
• Date: Wed, 15 Jan 2025 08:24:35 GMT
• Title: Joint Learning of Depth and Appearance for Portrait Image Animation
• Authors: Xinya Ji, Gaspard Zoss, Prashanth Chandran, Lingchen Yang, Xun Cao, Barbara Solenthaler, Derek Bradley,
• Abstract summary: We propose to jointly learn the visual appearance and depth simultaneously in a diffusion-based portrait image generator. Our framework can be efficiently adapted to various downstream applications, such as facial depth-to-image and image-to-depth generation.
• Score: 20.83495988491606
• License:
• Abstract: 2D portrait animation has experienced significant advancements in recent years. Much research has utilized the prior knowledge embedded in large generative diffusion models to enhance high-quality image manipulation. However, most methods only focus on generating RGB images as output, and the co-generation of consistent visual plus 3D output remains largely under-explored. In our work, we propose to jointly learn the visual appearance and depth simultaneously in a diffusion-based portrait image generator. Our method embraces the end-to-end diffusion paradigm and introduces a new architecture suitable for learning this conditional joint distribution, consisting of a reference network and a channel-expanded diffusion backbone. Once trained, our framework can be efficiently adapted to various downstream applications, such as facial depth-to-image and image-to-depth generation, portrait relighting, and audio-driven talking head animation with consistent 3D output.

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