Yisheng He (何益升)

Yisheng He is a researcher at Alibaba. He obtained his Ph.D. at HKUST, advised by Prof. Qifeng Chen, Prof. Long Quan, and Dr. Jian Sun.

Email  /  Google Scholar  /  GitHub

🔥We are now actively hiring research interns. If you are interested in working with me, feel free to email me your CV.

I'm interested in 3D Computer Vision, AIGC, Embodied AI, and Digital Avatar.

Forge4D is the first feed-forward model for 4D human Gaussian reconstruction in real world metric scale, and enables novel-view and novel-time synthesis from uncalibrated sparse-view videos in an efficient streaming manner.

PanoLAM is a large avatar model for Gaussian full-head reconstruction from a single unposed image. It utilize coarse-to-fine and dual-branch frameworks that creates Gaussian full-head within a second.

CoProSketch provides prominent controllability and details for sketch generation with diffusion models.

LAM creates animatable Gaussian heads with one-shot images in a single forward pass, which can be reenacted and rendered on various platforms (including mobile phones) in real time.

LaMP is a language-motion pretraining model that advances text-to-motion generation, motion-text retrieval, and motion captioning through aligned language-motion representation learning.

We build a bidirectional control flow between the style and the content for stylized motion generation and enable multimodal style control including text, image, and style motions.

We introduce a hybrid framework that leverages 3D Gaussian representation to advance physical property identification.

We introduce a 2D joint VQVAE to quantize each joint instead of all joints into tokens. A spatial-temporal modeling framework with temporal-spatial 2D masking and 2D attention is also proposed for motion generation.

We enable high-fidelity, transferable, and intensity control for neural field editing.

Our method synthesizes consistent 3D content with fine-grained sketch control.

We introduce a new problem: open-vocabulary 9D object pose and size estimation, a new dataset: OO3D-9D, and a new framework based on vision foundation model to tackle this problem.

A self-supervised framework for category-level object pose and size estimation via differentiable shape deformation, registration, and rendering.

A new open-set few-shot 6D object pose estimation problem: estimating the 6D pose of an unknown object by a few support views without CAD models and extra training. A large-scale synthesis dataset for pre-training and benchmarks for future research.

A generic full flow bidirectional fusion framework for RGBD representation learning, applied to joint instance semantic segmentation and 3D keypoint-based 6D pose estimation.

A brand new dataset to promote the study of instance segmentation for objects with irregular shapes and an affinity-based algorithm to tackle it.

The first deep learning 3D keypoint-based 6D pose estimation algorithm and an overall framework for joint instance semantic segmantation and 3D keypoint detection.

Last updated: October, 2025.

Thanks Dr. Jon Barron for sharing the template code.