Recently, extensive research on image customization (e.g., identity, subject, style, background, etc.) demonstrates strong customization capabilities in large-scale generative models. However, most approaches are designed for specific tasks, restricting their generalizability to combine different types of condition. Developing a unified framework for image customization remains an open challenge. In this paper, we present DreamO, an image customization framework designed to support a wide range of tasks while facilitating seamless integration of multiple conditions. Specifically, DreamO utilizes a diffusion transformer (DiT) framework to uniformly process input of different types. During training, we introduce a feature routing constraint to facilitate the precise querying of relevant information from reference images. Additionally, we design a placeholder strategy that associates specific placeholders with conditions at particular positions, enabling control over the placement of conditions in the generated results. Moreover, we employ a progressive training strategy to ensure smooth model convergence and correct the generation quality of the final output. Extensive experiments demonstrate that the proposed DreamO can effectively perform various image customization tasks with high quality and flexibly integrate different types of control conditions. Project page: https://mc-e.github.io/project/DreamO

Abstract In this work, we introduce a new approach for face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high‐quality artistic faces with diverse styles and accurate facial reconstruction. Our proposed framework, MMFS, supports multi‐modal face stylization by leveraging the strengths of StyleGAN and integrates it into an encoder‐decoder architecture. Specifically, we use the mid‐resolution and high‐resolution layers of StyleGAN as the decoder to generate high‐quality faces, while aligning its low‐resolution layer with the encoder to extract and preserve input facial details. We also introduce a two‐stage training strategy, where we train the encoder in the first stage to align the feature maps with StyleGAN and enable a faithful reconstruction of input faces. In the second stage, the entire network is fine‐tuned with artistic data for stylized face generation. To enable the fine‐tuned model to be applied in zero‐shot and one‐shot stylization tasks, we train an additional mapping network from the large‐scale Contrastive‐Language‐Image‐Pre‐training (CLIP) space to a latent w+ space of fine‐tuned StyleGAN. Qualitative and quantitative experiments show that our framework achieves superior performance in both one‐shot and zero‐shot face stylization tasks, outperforming state‐of‐the‐art methods by a large margin.

Abstract In this work, we present a new method for 3D face reconstruction from sparse‐view RGB images. Unlike previous methods which are built upon 3D morphable models (3DMMs) with limited details, we leverage an implicit representation to encode rich geometric features. Our overall pipeline consists of two major components, including a geometry network, which learns a deformable neural signed distance function (SDF) as the 3D face representation, and a rendering network, which learns to render on‐surface points of the neural SDF to match the input images via self‐supervised optimization. To handle in‐the‐wild sparse‐view input of the same target with different expressions at test time, we propose residual latent code to effectively expand the shape space of the learned implicit face representation as well as a novel view‐switch loss to enforce consistency among different views. Our experimental results on several benchmark datasets demonstrate that our approach outperforms alternative baselines and achieves superior face reconstruction results compared to state‐of‐the‐art methods.