DPBridge: Latent Diffusion Bridge for Dense Prediction

Diffusion models have demonstrated remarkable success in dense prediction problems, which aims to model per-pixel relationship between RGB images and dense signal maps, thanks to their ability to effectively capture complex data distributions. However, initiating the reverse sampling trajectory from uninformative noise prior introduces limitations such as degraded performance and slow inference speed. In this work, we propose DPBridge, a generative framework that formulates dense prediction tasks as image-conditioned generation problems and establishes a direct mapping between input image and its corresponding dense map based on fully-tractable diffusion bridge process. This approach addresses aforementioned limitations in conventional diffusion-based solutions. In addition, we introduce finetuning strategies to adapt our model from pretrained image diffusion backbone, leveraging its rich visual prior knowledge to facilitate both efficient training and robust generalization ability. Experimental results shows that our DPBridge can achieve competitive performance compared to both feed-forward and diffusion-based approaches across various benchmarks, highlighting its effectiveness and adaptability.

JADE: Joint-aware Latent Diffusion for 3D Human Generative Modeling

Generative modeling of 3D human bodies have been studied extensively in computer vision. The core is to design a compact latent representation that is both expressive and semantically interpretable, yet existing approaches struggle to achieve both requirements. In this work, we introduce JADE, a generative framework that learns the variations of human shapes with fined-grained control. Our key insight is a joint-aware latent representation that decomposes human bodies into skeleton structures, modeled by joint positions, and local surface geometries, characterized by features attached to each joint. This disentangled latent space design enables geometric and semantic interpretation, facilitating users with flexible controllability. To generate coherent and plausible human shapes under our proposed decomposition, we also present a cascaded pipeline where two diffusions are employed to model the distribution of skeleton structures and local surface geometries respectively. Extensive experiments are conducted on public datasets, where we demonstrate the effectiveness of JADE framework in multiple tasks in terms of autoencoding reconstruction accuracy, editing controllability and generation quality compared with existing methods.