With the explosive growth of 3D objects yet expensive annotation costs, unsupervised 3D object retrieval has become a popular but challenging research area. Existing labeled resources have been utilized to aid this task via transfer learning, which aligns the distribution of unlabeled data with the source one. However, the labeled resource are not always accessible due to the privacy disputes, limited computational capacity and other thorny restrictions. Therefore, we propose source-free model adaptation task for unsupervised 3D object management, which utilizes a pre-trained model to boost the performance with no access to source data and labels. Specifically, we compute representative prototypes to assume the source feature distribution, and design a bidirectional cumulative confidence-based adaptation strategy to adaptively align unlabeled samples towards prototypes. Subsequently, a dual-model distillation mechanism is proposed to generate source hypothesis for remedying the absence of ground-truth labels. The experiments on a cross-domain retrieval benchmark NTU-PSB (PSB-NTU) and a cross-modality retrieval benchmark MI3DOR also demonstrate the superiority of the proposed method even without access to raw data.

Recent researches reveal that StyleGAN can generate highly realistic images, inspiring researchers to use pretrained StyleGAN to generate high-fidelity swapped faces. However, existing methods fail to meet the expectations in two essential aspects of high-fidelity face swapping. Their results are blurry without pore-level details and fail to preserve identity for challenging cases. To overcome the above artifacts, we innovatively construct a series of identity-preserving semantic bases of StyleGAN (called StyleIPSB) in respect of pose, expression, and illumination. Each basis of StyleIPSB controls one specific semantic attribute and disentangles with the others. The StyleIPSB constrains style code in the subspace of W+ space to preserve pore-level details and gives us a novel tool for high-fidelity face swapping, and we propose a three-stage framework for face swapping with StyleIPSB. Firstly, we transform the target facial images' attributes to the source image. We learn the mapping from 3D Morphable Model (3DMM) parameters, which capture the prominent semantic variance, to the coordinates of StyleIPSB that show higher identity-preserving and fidelity. Secondly, to transform detailed attributes which 3DMM does not capture, we learn the residual attribute between the reenacted face and the target face. Finally, the face is blended into the background of the target image. Extensive results and comparisons demonstrate that StyleIPSB can effectively preserve identity and pore-level details. The results of face swapping can achieve state-of-the-art performance. We will release our code at https://github.com/a686432/StyleIPSB

3D morphable models (3DMMs) are generative models for face shape and appearance. Recent works impose face recognition constraints on 3DMM shape parameters so that the face shapes of the same person remain consistent. However, the shape parameters of traditional 3DMMs satisfy the multivariate Gaussian distribution. In contrast, the identity embeddings meet the hypersphere distribution, and this conflict makes it challenging for face reconstruction models to preserve the faithfulness and the shape consistency simultaneously. In other words, recognition loss and reconstruction loss can not decrease jointly due to their conflict distribution. To address this issue, we propose the Sphere Face Model (SFM), a novel 3DMM for monocular face reconstruction, preserving both shape fidelity and identity consistency. The core of our SFM is the basis matrix which can be used to reconstruct 3D face shapes, and the basic matrix is learned by adopting a two-stage training approach where 3D and 2D training data are used in the first and second stages, respectively. We design a novel loss to resolve the distribution mismatch, enforcing that the shape parameters have the hyperspherical distribution. Our model accepts 2D and 3D data for constructing the sphere face models. Extensive experiments show that SFM has high representation ability and clustering performance in its shape parameter space. Moreover, it produces high-fidelity face shapes consistently in challenging conditions in monocular face reconstruction. The code will be released at https://github.com/a686432/SIR

Abstract Many recent works have reconstructed distinctive 3D face shapes by aggregating shape parameters of the same identity and separating those of different people based on parametric models (e.g. 3D morphable models (3DMMs)). However, despite the high accuracy in the face recognition task using these shape parameters, the visual discrimination of face shapes reconstructed from those parameters remains unsatisfactory. Previous works have not answered the following research question: Do discriminative shape parameters guarantee visual discrimination in represented 3D face shapes? This paper analyses the relationship between shape parameters and reconstructed shape geometry, and proposes a novel shape identity‐aware regularization (SIR) loss for shape parameters, aiming at increasing discriminability in both the shape parameter and shape geometry domains. Moreover, to cope with the lack of training data containing both landmark and identity annotations, we propose a network structure and an associated training strategy to leverage mixed data containing either identity or landmark labels. In addition, since face recognition accuracy does not mean the recognizability of reconstructed face shapes from the shape parameters, we propose the SIR metric to measure the discriminability of face shapes. We compare our method with existing methods in terms of the reconstruction error, visual discriminability, and face recognition accuracy of the shape parameters and SIR metric. Experimental results show that our method outperforms the state‐of‐the‐art methods. The code will be released at https://github.com/a686432/SIR .