Portrait Relighting with Diffusion Models: New Paths in Image Synthesis

Manipulating and controlling lighting conditions in portrait images is a challenging task in computer graphics and image editing. A new approach based on diffusion models promises progress here and opens up exciting possibilities for realistic and creative image design. The focus is on the idea of considering relighting as a re-rendering problem, where pixels are transformed in response to changing ambient lighting conditions.

Synthetic Training Data and the Bridge to Reality

A central aspect of this new method is the use of synthetic training data. Using physically-based rendering engines, datasets are created that simulate the light-dependent transformation of 3D face models under various lighting conditions. This approach bypasses the need to create complex and expensive light stage recordings of real people.

The challenge is to bridge the gap between synthetic and real images. Two strategies are pursued for this:

1. Multi-Task Training: By integrating real portrait images without lighting information into the training process, the model learns to produce more realistic results. 2. Classifier-Free Guidance: During inference, i.e., the application of the trained model to new images, the input portrait is used to better preserve details and achieve more realistic lighting effects.

Advantages and Potentials

The diffusion model-based method shows promising results. It enables realistic lighting effects, including specular highlights and cast shadows, while preserving the identity of the person depicted. Quantitative experiments with light stage data show that the results are comparable to established relighting methods. Qualitative results on images from the internet demonstrate the model's ability to handle complex lighting scenarios.

The use of synthetic data simplifies the training process and allows for the generation of large datasets with precise lighting information. This opens up new possibilities for the development of more robust and generalizable relighting models. Furthermore, the approach offers the possibility to specifically control lighting effects and perform creative image manipulations.

Applications and Future Developments

The described method has the potential to be applied in various fields, including:

1. Photography and Image Editing: Realistic adjustment of lighting in portraits, improvement of image quality, and creative design options. 2. Film and Video Production: Efficient and cost-effective post-processing of lighting scenes, virtual lighting of actors. 3. Virtual Reality and Augmented Reality: Realistic representation of avatars and virtual environments with dynamic lighting. 4. Medical Imaging: Improvement of the visualization of medical images by adjusting the lighting.

Future research could focus on improving the generalizability of the models, integrating more complex light models, and developing interactive tools for controlling lighting. The combination of diffusion models with other AI techniques, such as facial recognition and 3D reconstruction, opens up further exciting perspectives for the future of image synthesis.

Mindverse: AI Solutions for Content Creation

Mindverse, a German provider of AI-powered content tools, offers a comprehensive platform for the creation of texts, images, and videos. With customized solutions such as chatbots, voicebots, AI search engines, and knowledge systems, Mindverse supports companies in optimizing their content strategy. The development of innovative technologies in the field of image synthesis, such as the portrait relighting method presented here, plays an important role in this.

Bibliographie: Chaturvedi, S., Ren, M., Hold-Geoffroy, Y., Liu, J., Dorsey, J., & Shu, Z. (2025). SynthLight: Portrait Relighting with Diffusion Model by Learning to Re-render Synthetic Faces. arXiv preprint arXiv:2501.09756. Yeh, Y.-Y., Nagano, K., Khamis, S., Kautz, J., Liu, M.-Y., & Wang, T.-C. (2022). Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation. arXiv preprint arXiv:2209.10510. Ponglertnapakorn, P., Tritrong, N., & Suwajanakorn, S. (2023). DiFaReli: Diffusion Face Relighting. arXiv preprint arXiv:2304.09479. NVIDIA Research. Lumos. https://research.nvidia.com/labs/dir/lumos/ Ponglertnapakorn, P., Tritrong, N., & Suwajanakorn, S. (2023). DiFaReli: Diffusion Face Relighting. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 14435-14444). Mei, Y. LightPT. https://yiqunmei.net/lightpt/ Cai, R., Zhang, G., Wang, Z., & Yu, J. (2024). LightPainter: Interactive Portrait Relighting with Freehand Scribbles. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.