5 accepted papers
Existing text-to-motion (T2M) generation models typically rely on pretrained large language models to encode textual inputs. However, these models, trained on generic text corpora, lack explicit alignment between motion-related words (e.g., "clockwise'', "quickly'') and human skeletal movements. Thi…
In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain. As an important tool of regularizing the distribution, batch normalization (BN) has been widely used in existing methods. However, they n…
Knowing the 3D locations of the surrounding vehicles is of vital importance in autonomous driving scenarios. It can be pretty challenging to make an accurate estimation from a monocular moving camera. In this paper, we present an effective vehicle 3D localization method, that utilizes 2D key-points…
Low-resolution person re-identification (LR re-id) is a challenging task with low-resolution probes and high-resolution gallery images. To address the resolution mismatch, existing methods typically recover missing details for low-resolution probes by super-resolution. However, they usually pre-spec…
Even though most existing monocular 3D pose estimation approaches achieve very competitive results, they ignore the heterogeneity among human body parts by estimating them with the same network architecture. To accurately estimate 3D poses of different body parts, we attempt to build a part-aware 3D…