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Dongliang Wang

5 accepted papers

2026

Neural Collapse Priors Driven Trust Semi-Supervised Multi-View Classification

AAAI 2026technical

In semi‑supervised multi‑view classification (SMVC), scarce labels and noisy unlabeled data impair feature aggregation and compromise prediction reliability, while existing methods lack principled guidance and interpretability. To overcome these limitations, we propose a novel unified SMVC framework

Cited by 0SourcePDFScholar
2023

ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generation

ICCV 2023poster

We present a GAN-based Transformer for general action-conditioned 3D human motion generation, including not only single-person actions but also multi-person interactive actions. Our approach consists of a powerful Action-conditioned motion TransFormer (ActFormer) under a GAN training scheme, equippe…

Cited by 74PDFScholar
2022

L-Tracing: Fast Light Visibility Estimation on Neural Surfaces by Sphere Tracing

ECCV 2022poster

"We introduce a highly efficient light visibility estimation method, called L-Tracing, for reflectance factorization on neural implicit surfaces. Light visibility is indispensable for modeling shadows and specular of high quality on object’s surface. For neural implicit representations, former metho…

Cited by 11SourcePDFScholar
2022

Learning Video Representations of Human Motion From Synthetic Data

CVPR 2022poster

In this paper, we take an early step towards video representation learning of human actions with the help of largescale synthetic videos, particularly for human motion representation enhancement. Specifically, we first introduce an automatic action-related video synthesis pipeline based on a photore…

Cited by 17PDFScholar
2021

Temporal Context Aggregation Network for Temporal Action Proposal Refinement

CVPR 2021poster

Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field. The proposals generated by current methods still suffer from inaccurate temporal boundaries and inferior confidence used…

Cited by 166PDFScholar