Joint Learning of Depth and Appearance for Portrait Image Animation (2501.08649v1)

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.

• The paper introduces a novel diffusion model that jointly learns RGB and depth outputs for realistic portrait animation.
• It employs a six-channel architecture with ReferenceNet to maintain visual identity and achieves superior monocular depth estimation with an AbsRel of 0.162.
• This unified framework supports enhanced portrait manipulation tasks such as relighting and audio-driven animation for 3D-aware applications.

Joint Learning of Depth and Appearance for Portrait Image Animation

The paper "Joint Learning of Depth and Appearance for Portrait Image Animation" addresses the problem of simultaneously generating high-quality visual and depth outputs from 2D portrait images. This research is driven by the need for more sophisticated portrait image manipulation that goes beyond traditional RGB image synthesis by incorporating 3D depth information. Such dual-output capability has substantial implications for applications in computer vision and graphics, including relighting, expression manipulation, and 3D-aware animation.

Methodology Overview

The authors propose a novel generative model architecture that leverages diffusion models to learn a joint distribution of appearance and depth from portrait images. This model is built upon the distillation of knowledge from pre-trained stable diffusion models, adapted to co-generate RGB images and corresponding depth maps. The key innovation lies in expanding a traditional diffusion backbone to handle six-channel inputs, enabling simultaneous de-noising of RGB and depth latent variables.

A critical component of this work is the introduction of a reference network, ReferenceNet, which extracts identity features from an RGB reference image to condition the image diffusion process. This ensures that the synthesized image maintains consistency in visual identity corresponding to the reference image.

During training, the model is exposed to a mixture of studio-captured face images with associated ground truth 3D geometry and in-the-wild facial videos with estimated 3D structure. This combination allows the model to accurately learn depth generation, improving generalizability across diverse real-world scenarios.

Experimental Results

The model's effectiveness is validated through extensive experiments involving monocular depth estimation and depth-conditioned image generation. The depth estimation capability is particularly noteworthy, as the paper reports superior performance against several state-of-the-art methods, including leading models like Sapiens. The proposed approach demonstrated an Absolute Relative Error (AbsRel) of 0.162, surpassing the 0.197 achieved by Sapiens-1B, indicating enhanced accuracy in predicting realistic depth maps.

Furthermore, the authors demonstrate the adaptability of their framework to other portrait manipulation tasks, such as relighting and audio-driven animation. The ability to jointly generate and employ depth information enables nuanced control over post-processing effects like relighting and providing stable, depth-consistent video frame outputs in talking head applications.

Implications and Future Work

The fusion of RGB and depth modeling in a single generative framework represents a significant advance in portrait image synthesis. By embracing both spatial and temporal dimensions, this research lays the groundwork for future exploration into more complex dynamics of image manipulation, potentially advancing towards fully automated 3D portrait creation from 2D inputs.

Practical applications arising from this work could transform fields such as virtual reality, gaming, and even telepresence, where immersion and realism rely heavily on accurate 3D representations. The model's efficiency and robustness also pose it as a critical tool in enhancing real-time facial animation and expression transfer capabilities.

Moving forward, future research might investigate scaling this method across larger and more diverse datasets or adapting the architecture for higher-resolution outputs. Moreover, integrating this framework with real-time audio processing systems could enhance its applicability in live settings, creating seamless, interactive user experiences.

In conclusion, this paper introduces a pioneering approach to joint depth and appearance learning, paving the way for more comprehensive and realistic portrait image manipulation and animation solutions. The findings emphasize the potential for diffusion models to unify distinct visual domains, promising substantial advancements in both academic research and industrial applications.