We propose OwlSight, a biomimetic-inspired framework with whole-stage illumination enhancement to interact with action classification for accurate dark video human action recognition.

In this work, we propose Expressive Keypoints that incorporates hand and foot details to form a fine-grained skeletal representation, to improve the discriminative ability for existing models in discerning intricate human actions.

In this work, we propose a novel zero-shot 3D HOI generation framework without training on specific datasets, leveraging the knowledge from large-scale pre-trained models.

In this work, we introduce a novel two-stage framework that employs scene affordance as an intermediate representation, effectively linking 3D scene grounding and conditional motion generation. Our framework comprises an Affordance Diffusion Model (ADM) for predicting explicit affordance map and an Affordance-to-Motion Diffusion Model (AMDM) for generating plausible human motions.

We propose PHRIT, a novel approach for parametric hand mesh modeling with an implicit template that combines the advantages of both parametric meshes and implicit representations. Our method represents deformable hand shapes using signed distance fields (SDFs) with part-based shape priors, utilizing a deformation field to execute the deformation.

We propose MixSTE (Mixed Spatio-Temporal Encoder), which has a temporal transformer to separately model the temporal motion of each joint and a spatial transformer to learn inter-joint spatial correlation.

We propose an uncertainty-aware method to quantify and optimize the depth and 2D detection input respectively.