Search Results for author: Fangzhou Hong

Found 9 papers, 7 papers with code

AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars

1 code implementation17 May 2022 Fangzhou Hong, Mingyuan Zhang, Liang Pan, Zhongang Cai, Lei Yang, Ziwei Liu

Our key insight is to take advantage of the powerful vision-language model CLIP for supervising neural human generation, in terms of 3D geometry, texture and animation.

Language Modelling Motion Synthesis +1

Versatile Multi-Modal Pre-Training for Human-Centric Perception

1 code implementation CVPR 2022 Fangzhou Hong, Liang Pan, Zhongang Cai, Ziwei Liu

To tackle the challenges, we design the novel Dense Intra-sample Contrastive Learning and Sparse Structure-aware Contrastive Learning targets by hierarchically learning a modal-invariant latent space featured with continuous and ordinal feature distribution and structure-aware semantic consistency.

Contrastive Learning Human Parsing +1

LiDAR-based 4D Panoptic Segmentation via Dynamic Shifting Network

1 code implementation14 Mar 2022 Fangzhou Hong, Hui Zhou, Xinge Zhu, Hongsheng Li, Ziwei Liu

In this work, we address the task of LiDAR-based panoptic segmentation, which aims to parse both objects and scenes in a unified manner.

Autonomous Driving Panoptic Segmentation

Garment4D: Garment Reconstruction from Point Cloud Sequences

1 code implementation NeurIPS 2021 Fangzhou Hong, Liang Pan, Zhongang Cai, Ziwei Liu

The main challenges are two-fold: 1) effective 3D feature learning for fine details, and 2) capture of garment dynamics caused by the interaction between garments and the human body, especially for loose garments like skirts.

LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local Region Contexts

no code implementations18 Mar 2020 Xinhai Liu, Zhizhong Han, Fangzhou Hong, Yu-Shen Liu, Matthias Zwicker

However, due to the irregularity and sparsity in sampled point clouds, it is hard to encode the fine-grained geometry of local regions and their spatial relationships when only using the fixed-size filters and individual local feature integration, which limit the ability to learn discriminative features.

Cannot find the paper you are looking for? You can Submit a new open access paper.