In the challenging realm of image-to-image translation, most traditional methods require separate models for different translation directions, leading to inefficient use of computational resources. This paper introduces the Bidirectional Brownian Bridge Diffusion Model (BiBBDM), a novel approach that leverages Brownian Bridge processes for bidirectional image-to-image translation. Unlike conventional Diffusion Models (DMs) that treat image-to-image translation as a unidirectional conditional generation process, BiBBDM models the translation as a stochastic Brownian Bridge process, enabling simultaneous learning of bidirectional translation between two domains. This innovation allows our method to achieve bidirectional image translation using different sampling directions of a single model, eliminating the need for multiple models for both translation directions. To the best of our knowledge, BiBBDM is the first image translation framework to achieve simultaneous dual-domain sampling with the same model and parameters, based on Brownian Bridge diffusion processes. Extensive experimental results on various benchmarks demonstrate that BiBBDM achieves competitive performance, as evidenced by both visual inspection and quantitative metrics.

Most of the existing 3D talking face synthesis methods suffer from the lack of detailed facial expressions and realistic head poses, resulting in unsatisfactory experiences for users. In this article, we propose a novel pose-aware 3D talking face synthesis method with a novel geometry-guided audio-vertices attention. To capture more detailed expression, such as the subtle nuances of mouth shape and eye movement, we propose to build hierarchical audio features including a global attribute feature and a series of vertex-wise local latent movement features. Then, in order to fully exploit the topology of facial models, we further propose a novel geometry-guided audio-vertices attention module to predict the displacement of each vertex by using vertex connectivity relations to take full advantage of the corresponding hierarchical audio features. Finally, to accomplish pose-aware animation, we expand the existing database with an additional pose attribute, and a novel pose estimation module is proposed by paying attention to the whole head model. Numerical experiments demonstrate the effectiveness of the proposed method on realistic expression and head movements against state-of-the-art methods.

Text-driven image editing aims to manipulate images with the guidance of natural language description. Text is much more natural and intuitive than many other interaction modes, and attracts more attention recently. However, compared with classical supervised learning tasks, there is no standard benchmark dataset for text-driven interactive image editing up to now. Therefore, it is hard to train an end-to-end model for pixel-aligned interactive image editing driven by text. Some methods follow the paradigm of text-to-image models by incorporating the target image into the process of text-to-image generation. However, these methods relying on cross-modal text-to-image generation involve complicated and expensive models, which can lead to inconsistent editing effects. In this article, a novel text-driven image editing method is proposed. Our key observation is that this task can be more efficiently learned using image-to-image translation. To ensure effective learning for image editing, our framework takes paired text and the corresponding images for training, and disentangles each image into content and attributes, such that the content is maintained while the attributes are modified according to the text. Our network is a lightweight encoder-decoder architecture that accomplishes pixel-aligned end-to-end training via cycle-consistent supervision. Quantitative and qualitative experimental results show that the proposed method achieves state-of-the-art performance.

Image-to-image translation is an important and challenging problem in computer vision and image processing. Diffusion models (DM) have shown great potentials for high-quality image synthesis, and have gained competitive performance on the task of image-to-image translation. However, most of the existing diffusion models treat image-to-image translation as conditional generation processes, and suffer heavily from the gap between distinct domains. In this paper, a novel image-to-image translation method based on the Brownian Bridge Diffusion Model (BBDM) is proposed, which models image-to-image translation as a stochastic Brownian Bridge process, and learns the translation between two domains directly through the bidirectional diffusion process rather than a conditional generation process. To the best of our knowledge, it is the first work that proposes Brownian Bridge diffusion process for image-to-image translation. Experimental results on various benchmarks demonstrate that the proposed BBDM model achieves competitive performance through both visual inspection and measurable metrics.

Three-dimensional (3-D) tracking in point cloud is a core competence of autonomous robots to perceive and forecast the environment. How to initialize bounding box seeds and optimize their position and orientation are very crucial for 3-D object tracking in point clouds. Nevertheless, existing methods mainly resort to developing a powerful classifier based on the initial bounding box seeds. In this article, we propose an end-to-end deep supervised descent method (SDM), which seamlessly integrates multiple seeds generation for the initialization of seeds and sequential updates for the estimation of accurate result. Specifically, we start with transforming the SDM iterative process into a trainable recurrent module. It explicitly learns a series of descent directions in the parameter space, to gradually optimize the initial seeds. Moreover, to alleviate drifting of this process, we initialize multiple seeds based on aggregated point sets generated by the deep Hough voting. Besides, a discrimination module is introduced to determine the bounding box with the highest score as the final result. Importantly, a specific multitask loss is proposed to train our model in an end-to-end way. Experiments on KITTI, PandaSet, and Waymo datasets show that our method could achieve significant improvements (up to 11.2% in success ratio) as compared to state-of-the-art trackers.