DiFaReli++: Diffusion Face Relighting with Consistent Cast Shadows

• URL: http://arxiv.org/abs/2304.09479v4
• Date: Sat, 25 Jan 2025 18:24:20 GMT
• Title: DiFaReli++: Diffusion Face Relighting with Consistent Cast Shadows
• Authors: Puntawat Ponglertnapakorn, Nontawat Tritrong, Supasorn Suwajanakorn,
• Abstract summary: We introduce a novel approach to single-view face relighting in the wild, addressing challenges such as global illumination and cast shadows. We propose a single-shot relighting framework that requires just one network pass, given pre-processed data, and even outperforms the teacher model across all metrics.
• Score: 11.566896201650056
• License:
• Abstract: We introduce a novel approach to single-view face relighting in the wild, addressing challenges such as global illumination and cast shadows. A common scheme in recent methods involves intrinsically decomposing an input image into 3D shape, albedo, and lighting, then recomposing it with the target lighting. However, estimating these components is error-prone and requires many training examples with ground-truth lighting to generalize well. Our work bypasses the need for accurate intrinsic estimation and can be trained solely on 2D images without any light stage data, relit pairs, multi-view images, or lighting ground truth. Our key idea is to leverage a conditional diffusion implicit model (DDIM) for decoding a disentangled light encoding along with other encodings related to 3D shape and facial identity inferred from off-the-shelf estimators. We propose a novel conditioning technique that simplifies modeling the complex interaction between light and geometry. It uses a rendered shading reference along with a shadow map, inferred using a simple and effective technique, to spatially modulate the DDIM. Moreover, we propose a single-shot relighting framework that requires just one network pass, given pre-processed data, and even outperforms the teacher model across all metrics. Our method realistically relights in-the-wild images with temporally consistent cast shadows under varying lighting conditions. We achieve state-of-the-art performance on the standard benchmark Multi-PIE and rank highest in user studies.

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