Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

In this paper, a novel Diffusion-based 3D Pose estimation (D3DP) method with Joint-wise
reProjection-based Multi-hypothesis Aggregation (JPMA) is proposed for probabilistic 3D …

The diffusion model has long been plagued by scalability and quadratic complexity issues,
especially within transformer-based structures. In this study, we aim to leverage the long …

Recovering a 3D human mesh from a single RGB image is a challenging task due to depth
ambiguity and self-occlusion, resulting in a high degree of uncertainty. Meanwhile, diffusion …

Translating images from a source domain to a target domain for learning target models is
one of the most common strategies in domain adaptive semantic segmentation (DASS) …

Abstract We propose a Unified Pose Sequence Modeling approach to unify heterogeneous
human behavior understanding tasks based on pose data, eg, action recognition, 3D pose …

In-context learning provides a new perspective for multi-task modeling for vision and NLP.
Under this setting the model can perceive tasks from prompts and accomplish them without …

We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating
cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively …

Estimating the 6D object pose from a single RGB image often involves noise and
indeterminacy due to challenges such as occlusions and cluttered backgrounds. Meanwhile …

Learning-based methods have dominated the 3D human pose estimation (HPE) tasks with
significantly better performance in most benchmarks than traditional optimization-based …