The widespread application of face recognition technology has exacerbated privacy threats. Face de-identification is an effective means of protecting visual privacy by concealing identity information. While deep learning-based methods have greatly improved de-identification results, most existing algorithms rely on 2D generative models that struggle to produce identity-consistent results for multiple views. In this paper, we focus on identity disentanglement within the latest 3D-aware face generation model, and propose an advanced face de-identification framework that can be applied to various scenarios. Our proposed framework disentangles identity from other facial features, modifies only the former and generates the de-identified face using a 3D generator. This approach results in high-quality, identity-consistent de-identification that preserves other facial features. We demonstrate our approach on StyleNeRF, one of the most widely-used style-based neural radiation field models. Through extensive experiments, we demonstrate the effectiveness of our approach in achieving face de-identification both for a single image and group images with the same identity. Our work is a significant step forward in the field of face de-identification, opening up new possibilities for practical applications.

In recent years, neural radiance fields have exhibited impressive performance in novel view synthesis. However, exploiting complex network structures to achieve generalizable NeRF usually results in inefficient rendering. Existing methods for accelerating rendering directly employ simpler inference networks or fewer sampling points, leading to unsatisfactory synthesis quality. To address the challenge of balancing rendering speed and quality in generalizable NeRF, we propose a novel framework, NeRF-SDP, which achieves both efficiency and high fidelity by introducing scene depth perception. We incorporate more scene information into the radiance field by using our proposed geometry feature extraction and depth-encoded ray transformer to improve the model’s inference capabilities with sparse points. With the aid of scene depth perception, NeRF-SDP can better understand the scene’s structure, thus better reconstructing the objects’ edges with significantly fewer artifacts. Experimental results demonstrate that NeRF-SDP achieves comparable synthesis quality to state-of-the-art methods while significantly improving rendering efficiency. Furthermore, ablation studies confirm that the depth-encoded ray transformer enhances the model’s robustness to varying numbers of sampling points.

Emerging interests have been brought to blind quality assessment for images/videos captured in the wild, known as in-the-wild I/VQA. Prior deep learning based approaches have achieved considerable progress in I/VQA, but are intrinsically troubled with two issues. Firstly, most existing methods fine-tune the image-classification-oriented pre-trained models for the absence of large-scale I/VQA datasets. However, the task misalignment between I/VQA and image classification leads to degraded generalization performance. Secondly, existing VQA methods directly conduct temporal pooling on the predicted frame-wise scores, resulting in ambiguous inter-frame relation modeling. In this work, we propose a two-stage architecture to separately predict image and video quality in the wild. In the first stage, we resort to supervised contrastive learning to derive quality-aware representations that facilitate the prediction of image quality. Specifically, we propose a novel quality-aware contrastive loss to pull together samples of similar quality and push away quality-different ones in embedding space. In the second stage, we develop a Relation-Guided Temporal Attention (RTA) module for video quality prediction, which captures global inter-frame dependencies in embedding space to learn frame-wise attention weights for frame quality aggregation. Extensive experiments demonstrate that our approach performs favorably against state-of-the-art methods on both authentically distorted image benchmarks and video benchmarks.