EVA3D is a high-quality unconditional 3D human generative model that only requires 2D image collections for training.

A large-scale multi-modal (color images, point clouds, keypoints, SMPL parameters, and textured meshes) 4D human dataset with 1000 human subjects, 400k sequences and 60M frames.

AvatarCLIP empowers layman users to customize a 3D avatar with the desired shape and texture, and drive the avatar with the described motions using solely natural languages.

The first to leverage the multi-modal nature of human data (e.g. RGB, depth, 2D key-points) for effective human-centric representation learning.

The first attempt at separable and interpretable garment reconstruction from point cloud sequences, especially challenging loose garments.

Rank 1st in the public leaderboard of SemanticKITTI panoptic segmentation (2020-11-16); A learnable clustering module is designed to adapt kernel functions to complex point distributions.

Rank 1st in the public leaderboard of SemanticKITTI semantic segmentation (2020-11-16); Cylindrical 3D convolution is designed to explore the 3D geometric pattern of LiDAR point clouds. Further extend the cylindrical convolution to more general LiDAR-based perception tasks.

To learn discriminative features on point clouds by encoding the fine-grained contexts inside and among local regions simultaneously.

Reconstruct human NeRF from a single image in one forward pass!

ReMoDiffuse is a diffusion-model-based motion generation framework that integrates a retrieval mechanism to refine the denoising process, which enhances the generalizability and diversity.

The first diffusion-model-based text-driven motion generation framework with probabilistic mapping, realistic synthesis and multi-level manipulation ability.

Extension of the CVPR21 Version; Extend DS-Net to 4D panoptic LiDAR segmentation by the temporally unified instance clustering on aligned LiDAR frames.